Abstract:
The present invention is related to systems and methods for translating language text on a mobile camera device offline without access to the Internet. More specifically, the present invention relates to systems and methods for displaying text of a first language and a translation of the first language text into a second language text which is displayed in real time in augmented reality on the mobile device. The processing can use a single line or a multiline algorithm designed with a plurality of processing innovations to insure accurate real-time translations without motion jitter. The invention may be used to help travelers in a foreign country with difficulties in reading and understanding text written in the local language of that country. The present invention may be utilized with wearable computers or glasses, producing seamless augmented reality foreign language translations. Some embodiments are particularly useful in translations from Asian languages to English.

Description:
REFERENCE TO RELATED APPLICATIONS 
     This application is a non-provisional and claims priority from provisional application U.S. Ser. No. 61/791,584, filed on Mar. 15, 2013, entitled “Recognition System,” the entirety of which is hereby incorporated by reference herein. 
    
    
     NOTICE OF COPYRIGHTS AND TRADEDRESS 
     A portion of the disclosure of this patent related document contains material which is subject to copyright protection. This patent related document may show and/or describe matter which is or may become tradedress of the owner. The copyright and tradedress owner has no objection to the facsimile reproduction by anyone of the patent disclosure as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright and tradedress rights whatsoever. 
     FIELD OF THE INVENTION 
     The present invention is generally related to systems and methods for translating Asian character sets. More specifically, the present invention relates to systems and methods for displaying Asian character sets and their translations in real time after image processing and recognition of Asian character sets on resource-constrained mobile devices. The present invention may be used to help travellers in a foreign country with difficulties in reading and understanding text written in the local language of that country. More generally, the present invention is also applicable to translations between any two languages. 
     BACKGROUND OF THE INVENTION 
     The statements in this section merely provide background information related to the present disclosure and may not constitute prior art. 
     Travelers in a foreign land often need to be able to read and understand some text written in a foreign language, such as a restaurant name or address, a restaurant menu, a street sign, a book, a map, a train schedule, or a newspaper. Conventionally a traveler may use a foreign translation book, hire a guide, or ask local people for help. These approaches are awkward and the increasing use of English by foreigners throughout the world as their second language is not going to end this language barrier. 
     Translating devices are known that use complex image processing and optical character recognition (OCR) software. OCR has significantly improved since its inception in the early 1990s and it is used on the Internet; however, foreign travelers generally do not have a mobile device with an Internet connection in a foreign country. Thus a translation device for a traveler needs to function adequately offline, that is, without the resources afforded by a connection to the Internet and access to an online server. 
     Offline OCR applications for mobile camera devices have size limitations in terms of size of the program code. There are limits to the speed of the image processing and OCR algorithms offline as well. There are limitations in types of processors and in memory resources in mobile camera devices. Offline mobile translator devices also suffer from a lack of translation accuracy and reproducibility. Generally mobile translation devices will be used to capture a single image frame of the foreign text to be translated. OCR will be performed on the captured image frame of the foreign text to translate the foreign language text into a language that can be read by the traveler. However, during image capture of the foreign text using a hand-held mobile camera device such as a smart phone, there are image capture problems which include camera movement, poor text image focus, and improper foreign text illumination. OCR requires a clear distinctive text image for an accurate and stable foreign text translation so a non-clear text image will mislead the OCR software, which will then produce a defective language translation. Thus it is known that offline translation apps for mobile camera devices such as smartphones frequently do not perform accurate and stable translations. The translations may fluctuate, jitter, or even make no sense at all. 
     For these reasons, there exists an important need for solutions to these problems related to current translation technology for mobile camera devices to bring improved speed, accuracy, and meaning in translations. There is a need for translations in real-time and with grammar linguistics to allow for a better touristic experience in a foreign land. What are needed are a method, system, and apparatus for rapid and meaningful translation of a foreign language text in real-time, on a resource-constrained mobile device, without the requirement for Internet connectivity. 
     Therefore, it would be an advancement in the state of the art to provide a method for rapid and accurate translation of a foreign language in real-time with accuracy to resolve the shortcomings of existing solutions. It would also be an advance in the state of the art to provide this translation method in a mobile device that can translate the foreign language in real-time without the need for Internet connectivity to automatically provide the tourist with meaningful information. It would be a further an advancement that such a translation is cost-efficient, does not require translators or dictionaries, or manual entering of text into the mobile device. It is against this background that various embodiments of the present invention were developed. 
     BRIEF SUMMARY OF THE INVENTION 
     Embodiments of the present invention include a method and a system for a translation of one or more words of a first language into one or more words of a second language using a mobile camera device. 
     Accordingly, and in accordance with an illustrative and preferred embodiment, the present invention in one aspect is a method for translating a video feed in real-time augmented reality from a first language to a second language using a mobile device, the mobile device comprising a video camera, a processor, a memory, and a display. The method in one aspect comprises the steps of (a) capturing a frame in real-time from the video feed of one or more words in the first language which need to be translated using the video camera to produce a captured frame; (b) cropping the captured frame to fit inside an image processing bounding box to produce a cropped frame; (c) pre-processing the cropped frame to produce a pre-processed frame; (d) performing character segment recognition on the pre-processed frame to produce a plurality of character segments; (e) performing character merging on the character segments to produce a plurality of merged character segments; (f) performing character recognition on the merged character segments to produce a recognized frame having a plurality of recognized characters; (g) processing the recognized frame through a translation engine to produce a translation of the recognized characters in the first language into one or more words of the second language to produce a translated frame, while also calculating a translation quality representing how well the recognized characters have been translated for each translated frame; (h) storing the translated frame to the memory as a current translated frame, wherein a previous translated frame is also stored in the memory; (i) checking that the bounding box has stayed on a same set of characters for the current translated frame and the previous translated frame by determining a fraction of similar characters that are overlapping between the current translated frame and the previous translated frame, wherein a higher fraction indicates that the bounding box has stayed on the same set of characters for the current translated frame and the previous translated frame; (j) comparing the translation quality determined by the translation engine for the current translated frame to a previous translation quality for the previous translated frame; (k) selecting one of the previous translated frame and the current translated frame to be removed from the memory based on a frame having a lower translation quality; and (l) displaying an optimal translated frame from the previous translated frame and the current translated frame, the optimal translated frame having a higher translation quality, wherein the words of the second language are overlaid over or next to the words in the first language which is being translated in an augmented reality on the display of the mobile device. In some embodiments, the translation quality is determined by how many and/or how well the one or more words of the first language are translated based on a translation engine score, also known simply as a translation score. This illustrative method may be embodied on a mobile device (such as a smartphone, a tablet, a wearable computer, a wearable eye glass, and/or a laptop computer), on a computer-readable storage medium, or transmitted via a network. 
     Accordingly, and according to one embodiment, the present invention is a method for a translation from a first language to a second language using a mobile camera device, the method comprising the steps of: (a) positioning the mobile camera device to display a video image of one or more words in the first language which need to be translated so that the mobile camera device can capture frames of a video feed of the one or more words in the first language for translation; (b) cropping the frames of the video feed to fit inside an image processing bounding box for image processing; (c) storing cropped frames of the video feed to a memory device; (d) pre-processing cropped frames of the video feed in the image processing bounding box; (e) performing character segment recognition on pre-processed frames of the video feed in the image processing bounding box; (f) performing horizontal merging with recognition feedback on character segment recognized frames of the video feed in the image processing bounding box; (g) performing binary or greyscale character recognition on horizontally merged character segment recognized frames of the video feed in the image processing bounding box; (h) processing character recognized frames of the video feed in the image processing bounding box for producing a translation of the one or more words in the first language into one or more words of the second language; (i) storing the one or more translated words of the second language to a location in the memory device as a current frame of a language translation video image; (j) checking that the image processing bounding box has stayed on the same first language text characters for the current frame and a previous frame of the language translation video image; (k) comparing information quality in the current frame of the language translation video image to the information quality in the previous frame of the language translation video image, wherein both the current frame of the language translation video image and the previous frame of the language translation video image are being saved in the memory device; (l) selecting one or more lower quality frames of the language translation video image to be deleted from storage in the memory device; and (m) using the mobile camera device for displaying one or more higher quality frames of the language translation video image of the one or more words of the second language while also displaying the video image of the one or more words in the first language which is being translated. 
     Another embodiment of the present invention also includes a method for displaying the one or more higher quality frames of the language translation video image of the one or more words of the second language in real time augmented reality. 
     Another embodiment of the present invention also includes a method for translating a first language selected from the group consisting of Chinese, Korean, Japanese, Vietnamese, Khmer, Lao, That, English, French, Spanish, German, Italian, Portuguese, Russian, Hindi, Greek, Hebrew, and Arabic. In some embodiments, the process can auto-detect which language is being presented in the video feed without the user having to select one. 
     Another embodiment of the present invention also includes a method for using a conversion table for converting dialects of the first language into a smaller number of dialects of the first language before translating the first language into the second language. 
     Another embodiment of the present invention also includes a method for using a conversion table to convert all traditional Chinese text characters to simplified Chinese text characters before translating the first language into the second language. 
     Another embodiment of the present invention also includes a method for obtaining a translation into a second language selected from the group consisting of Chinese, Korean, Japanese, Vietnamese, Khmer, Lao, That, English, French, Spanish, German, Italian, Portuguese, Russian, Hindi, Greek, Hebrew, and Arabic. 
     Another embodiment of the present invention also includes a method for selecting a single line of the first language or multiple lines of the first language for translation into the second language by changing a bounding box size on the mobile camera device which displays the video image of the first language. 
     Another embodiment of the present invention also includes a method for automatically moving the second language translation on the screen when the mobile camera device is moved without recalculating the translation. 
     Another embodiment of the present invention also includes pausing the language translation which is displayed on the mobile camera device to allow a movement of the mobile camera device without changing a displayed language translation. 
     Another embodiment of the present invention also includes storing a paused language translation comprising the first language and the translation of the first language into the second language in a memory device for a later review. 
     Another embodiment of the present invention also includes a method for comparing information quality in the current frame of the language translation video image to the information quality in the previous frame of the language translation video image, wherein the information quality of the language translation video image can be determined by how well the string of the first language is translated. 
     Another embodiment of the present invention also includes a method for checking that the image processing bounding box has stayed on the same first language text characters for the current frame and a previous frame of the language translation video image, the method comprising the steps of: (a) counting a number of similar language text characters in a current language text translation image string and in a previous language translation image string; and (b) calculating what fraction of these similar language text characters are overlapping in the current and the previous language translation image strings, wherein the higher the fraction, the greater the extent that the processing bounding box has stayed on the same language text for the current and the previous language translation text images. 
     Another embodiment of the present invention also includes a method for displaying a pronunciation of the one or more words of the first language being translated. 
     Another embodiment of the present invention is a computer system for translating a foreign language on a mobile camera device, the system comprising: a mobile camera for capturing a video image of the one or more words in the first language for translation of the first language text; a program code; a processor for processing the program code; one or more memories connected to the processor for storing the program code, which when executed by the processor causes the processor to execute a process, the process comprising the steps of: (a) positioning the mobile camera device to display a video image of one or more words in the first language which need to be translated so that the mobile camera device can capture frames of a video feed of the one or more words in the first language for translation; (b) cropping the frames of the video feed to fit inside an image processing bounding box for image processing; (c) storing cropped frames of the video feed to a memory device; (d) pre-processing cropped frames of the video feed in the image processing bounding box; (e) performing character segment recognition on pre-processed frames of the video feed in the image processing bounding box; (f) performing horizontal merging with recognition feedback on character segment recognized frames of the video feed in the image processing bounding box; (g) performing binary or greyscale character recognition on horizontally merged character segment recognized frames of the video feed in the image processing bounding box; (h) processing character recognized frames of the video feed in the image processing bounding box for producing a translation of the one or more words in the first language into one or more words of the second language; (i) storing the one or more translated words of the second language to a location in the memory device as a current frame of a language translation video image; (j) checking that the image processing bounding box has stayed on the same first language text characters for the current frame and a previous frame of the language translation video image; (k) comparing information quality in the current frame of the language translation video image to the information quality in the previous frame of the language translation video image, wherein both the current frame of the language translation video image and the previous frame of the language translation video image are being saved in the memory device; (l) selecting one or more lower quality frames of the language translation video image to be deleted from storage in the memory device; and (m) using the mobile camera device for displaying one or more higher quality frames of the language translation video image of the one or more words of the second language while also displaying the video image of the one or more words in the first language which is being translated. 
     The present invention also includes related system embodiments which include other methods of the present invention that could be carried out. Such a system could be implemented as a computer system embedded in a mobile camera device. Other features and advantages of the various embodiments of the present invention will be more apparent from the following more particular description descriptions of embodiments of the invention as illustrated in the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing summary, as well as the following detailed description of preferred embodiments of the invention, will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there is shown in the drawings embodiments which are presently preferred. It should be understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown. In the drawings: 
         FIG. 1A  illustrates process steps  100  to  120  of a flowchart of a process for translating a single line of a language in accordance with one embodiment of the present invention. 
         FIG. 1B  illustrates process steps  122  to  136  of the flowchart of the process of  FIG. 1A  for translating a single line of a language in accordance with one embodiment of the present invention. 
         FIG. 2A  illustrates a flowchart of a process for pre-processing cropped frames of the video feed in accordance with one embodiment of the present invention. 
         FIG. 2B  illustrates a flowchart of a process for performing character segment recognition in accordance with one embodiment of the present invention. 
         FIG. 2C  illustrates a flowchart of a process for performing binary character recognition on horizontally merged character segment recognized frames in accordance with one embodiment of the present invention. 
         FIG. 3A  illustrates process steps  302  to  318  of a flowchart of a process for translating multiple lines of a language in accordance with one embodiment of the present invention. 
         FIG. 3B  illustrates process steps  352  to  382  of the flowchart of the process from  FIG. 3A  for translating multiple lines of a language in accordance with one embodiment of the present invention. 
         FIG. 4  illustrates a flowchart of a process for multi-line recognition of cropped frames of the video feed in the image processing bounding box as a subroutine at blocks  368  and  376  of  FIG. 3B , in accordance with one embodiment of the present invention. 
         FIG. 5A  illustrates a flowchart of a process for multi-line recognition of a binary image having a light background and a dark text and for multi-line recognition of a binary image having a dark background and a light text in accordance with one embodiment of the present invention. 
         FIG. 5B  illustrates a flowchart of an alternative process for multi-line recognition on a binary image having a light background and a dark text as a subroutine and for recognition on a binary image having a dark background and a light text in accordance with one embodiment of the present invention. 
         FIG. 6A  illustrates process steps  602  to  612  of a flowchart of a process for performing a multi-line text cancellation after recognition on the binary image type with overlapping characters in accordance with one embodiment of the present invention. 
         FIG. 6B  illustrates process steps  652 - 660  of the flowchart of the process of  FIG. 6A  for performing a text cancellation after recognition on the binary image type with overlapping characters, in accordance with one embodiment of the present invention. 
         FIG. 7A  illustrates process steps  702  to  722  of a flowchart of a process for performing multi-line text grouping for each binary threshold type in accordance with one embodiment of the present invention. 
         FIG. 7B  illustrates process steps  752  to  784  of the flowchart of the process of  FIG. 7A  for performing multi-line text grouping for each binary threshold type in accordance with one embodiment of the present invention. 
         FIG. 8  depicts an illustrative Chinese restaurant menu with Chinese characters needing a multi-line language translation and a single line language translation, in accordance with one embodiment of the present invention. 
         FIG. 9  depicts an example of a user interface of a mobile camera device being used to increase a size of a bounding box by touching fingertip to a tab icon at the bottom of the bounding box and sliding the fingertip downward, in accordance with one embodiment of the present invention. 
         FIG. 10  depicts an example of a result of the activity depicted in  FIG. 9  in that the size of the bounding box has been increased in  FIG. 10  compared to  FIG. 9 , in accordance with one embodiment of the present invention. 
         FIG. 11  depicts an example of a user interface of a mobile camera device displaying algorithm-generated characters of the first language Chinese characters in the bounding box, and displaying below the bounding box the translation of the first language Chinese characters into the second language, in accordance with one embodiment of the present invention. 
         FIG. 12  depicts an example of a user interface of a mobile camera device displaying multiple lines of a translation of Chinese characters (faded) with an English translation (in bold) inside a bounding box, in accordance with one embodiment of the present invention. 
         FIG. 13  depicts a portion of  FIG. 12  in which the first language Chinese characters are more readily seen as would be the case when a user is practicing one embodiment of the present invention. 
         FIG. 14  depicts a portion of  FIG. 12  which is displaying a pronunciation of the first language Chinese characters, in accordance with another embodiment of the present invention. 
         FIG. 15  illustrates various alternative end-user devices which may utilize embodiments of the present invention, including smart phones and wearable computers. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention in one embodiment is a method and a system for using a mobile camera device to provide a translation of a first language into a second language in real-time. The invention in one embodiment is an application operating on a smartphone, using camera elements and software of the smartphone to focus on printed object text in one language, which text may then be seen in a display of the smartphone, and translating the object text in the one language to text in another language. The translated text is displayed to the user in the same display, and proximate the display of the object text. In one implementation the translated text is seen to float over the displayed object text. In alternative embodiments the invention may operate on digital devices other than smartphones. For example, some embodiments may be compatible with iPads, laptop computers, and other computerized appliances. In one embodiment the digital device may be computerized eyeglasses, wherein a wearer of the glasses, observing text in one language, may see text in another language superimposed proximate the original text. In some embodiments functionality may be entirely local to the digital device, and the device may operate off-line practicing embodiments of the invention. In other embodiments a digital device enabled to practice the invention may open a data connection to an on-line server, and some functionality may be provided by software and data at the on-line server. 
     When one or more lines of the first language have been selected for translation, then the processing system of the present invention places the selected first language text in focus. This enables the user to more readily position the mobile camera target box view of the first language text to be translated. In some embodiments, the focusing of the first language text in the target box is an automatic process. There optionally may be additional focusing methods including tapping a location of the mobile camera device. In some embodiments a light source is used to illuminate the first language text to aid in its focusing, processing, and translating. In some embodiments there is a zoom control for shrinking on the display which can shrink the selected text to fit in the target box. The zoom may also be used to expand text in the target box to a minimum average size necessary for text processing leading to a translation. Once the first language text is located within the target box, then the text will be made available for processing and translating into a second language text. The words of the first language viewed in the bounding box of the mobile camera device are the words that are translated into the second language. 
     In some embodiments the target box is sized to contain a single line of a first language text. In this case the translation into the second language text is displayed outside the target box. In another embodiment the user interface displays a pronunciation of the first language text. If the image of the first language in the bounding box is too dark, then a light on the mobile camera device, or another illumination source can be used to perform a better translation. 
     Methods and systems of the present invention have high level algorithm processing which creates accurate, less jittery translations. Contemplated examples of first and second languages that may be involved in practicing the present invention include languages selected from the group consisting of Chinese, Korean, Japanese, Vietnamese, Khmer, Lao, That, English, French, Spanish, German, Italian, Portuguese, Russian, Hindi, Greek, Hebrew, and Arabic. Preferred languages involved in practicing the present invention include translating the Asian languages, particularly Chinese, Korean, and Japanese. A particularly preferred practice of the present invention involves methods and systems for translating Chinese into English. Other human languages not listed here are also contemplated to be within the scope of the present invention, as would be recognized by one of ordinary skill in the art. 
     For some embodiments of the present invention, contextual information for translation processing is used to a degree which does not affect translation processing speed. For example, in the case of food translations, the food terms can be clustered by extracting ingredients so any prefix or suffix nearby can be clustered together in order to prevent wrong concatenation in translation. The algorithms of the present invention in some embodiments avoid translating single characters that are not food terms when the string is determined as a food item. Such programming controls for the possibility that single characters could possibly be wrong due to the nature of OCR results. Words of multiple characters have much lower chance of being wrong. A word in a first language can have multiple translations in a second language because of context in which the word is used, particularly when the word has multiple meanings in the first language or for fluidity of translation in to the second language. In one preferred embodiment, the invention processes give priority to food translation and then to signs and travel translations. 
     In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the invention can be practiced without these specific details. In other instances, structures, devices, activities, and methods are shown using schematic, use case, and/or flow diagrams in order to avoid obscuring the invention. 
     Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearance of the phrases “in one embodiment” in various places in the specification is not necessarily all referring to the same embodiment, nor is a separate or alternative embodiment mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not other embodiments. 
     Although the following description contains many specifics for the purposes of illustration, anyone skilled in the art will appreciate that many variations and/or alterations to suggested details are within the scope of the present invention. Similarly, although many of the features of the present invention are described in terms of each other, or in conjunction with each other, one skilled in the art will appreciate that many of these features can be provided independently of other features. Accordingly, this description of the invention is set forth without any loss of generality to, and without imposing limitations upon, the invention. 
     Definitions 
     As used herein, the term “first language” refers to the language that is translated by a mobile camera device using an embodiment of the present invention. The word or words of the first language to be translated need to appear in focus in the target box of the mobile camera device before any translation can occur. 
     As used herein, the term “second language” means the language in which the translation is displayed by a mobile camera device using an embodiment of the present invention. The translation in the second language is displayed as an augmented reality image on the mobile camera device. 
     As used herein, the term “translation” refers to a language translation, more particularly to a language text translation involving the translation of a first language text into a second language text. In this context, the term “translation” means a process for rendering a word text of a first language into a word text of a second language having the same meaning. As previously indicated, words, or phrases of the first language can appear to a user of the present invention in various venues and forms, including printed words of a restaurant menu, book, a train schedule, a street sign, a store sign, and the like. The text communication of the second language can be read by the user on the display of the mobile camera device as illustrated in  FIGS. 11-14 . 
     As used herein, “augmented reality” means a computer-mediated reality through the use of a wearable computer or hand-held device such as a smartphone, wherein the computer is used to add or subtract information from, or otherwise manipulate one&#39;s perception of reality. Typically, it is the user&#39;s visual perception of the environment that is mediated. This is done through the use of some kind of electronic device, such a smartphone, which can act as a visual filter between the real world and what the user perceives. Examples of wearable computers include GOOGLE GLASS™, and the like. 
     As used herein, the term “target box” is a viewfinder box on the user interface of the mobile camera device. The target box height can be set to permit viewing and translating only a single line of a first language text as shown in  FIG. 11  with target box  1104 . The target box height can be increased to permit viewing and translating multiple lines of the first language text as depicted in  FIG. 13  with target box  1306 . The present invention processes first language words appearing in the target box for translation. 
     The present invention can perform a language text translation in real-time. As used herein, “real-time” means in real-time or near real-time, where the user can view the translation without a significant time delay. Real-time does not necessarily mean instantaneously in the mathematical or physical sense, but only appears instantaneously to the user. 
     As used herein, “character” means conventional text features of the first language text which would be recognized visually as a letter, letters, a word, words, a character, characters, a character set, character sets, or any other term relating to a language text. 
     As used herein, “video feed” means the frames of video images. 
     As used herein, “mobile camera device” means a portable hardware device which has a camera which functions with a processor, a memory device, and a program code (application) as a system and for accomplishing methods for using the present invention. 
     As used herein, “stored frames” means saved digital information in a memory device of multiple captured images (i.e., frames) from a video camera. 
     As used herein, “greyscale” means a greyscale or greyscale digital image that is an image in which the value of each pixel is a single sample, that is, it carries only luminosity intensity information. Also images of this sort are known as black-and-white, and are known to be composed of shades of grey, varying from black at the weakest intensity to white at the strongest intensity. 
     As used herein, “colorscale” means an image color scale that may be used on a computing device. It is known that personal computers typically have 24-bit color depth, but the color depth will vary with device capabilities. 
     As used herein, “translation engine” means a system involving a processor with a memory device storing a program code, wherein the processor executes the program code for running a program performing a translation. 
     As used herein, “connected component analysis (CCA)” means an analysis used in image processing applications to partition an image into its segments. An image has segments consisting of sets of connected components, wherein the connected components are regions in the image having fields of pixels which are either all black or all white. In connected components, the fields of pixels are not separated by boundaries. 
     As used herein, “de-noising” means removing random pixels with no relationship to the connected components comprising fields of pixels which are either all black or all white. This de-noising follows the connected component analysis, which identifies the fields of pixels which are either all black or all white. 
     As used herein, “current frame” means a processed video image frame that is the second of two processed video image frames and is the video frame most recently translated. 
     As used herein, “previous frame” means a processed video image frame that is the first of two processed video image frames and is the video frame stored in a memory device as the current frame is being processed. 
     As used herein, “information quality” refers to an assessment of the words appearing in the second language text as a translation in relation to the number of words in the first language text to be translated. 
     As used herein, “lower quality frame” means a low assessment of words appearing in the second language text as a translation in relation to the number of words in the first language text to be translated. 
     As used herein, “higher quality frame” means a high assessment of words appearing in the second language text as a translation in relation to the number of words in the first language text to be translated. 
     As used herein, “image string” means one passage of a video frame image of the first language text through a process of the present invention. 
     As used herein, “blank string” means one passage of a video frame image of the first language text through an algorithm of the present invention which results in no second language text translation. 
     As used herein, “horizontally overlapping” means two separate text precursors have portions that have a different vertical coordinates but have common horizontal coordinates with respect to a center horizontal line of the video image frame. 
     As used herein, “vertically merging” means combining text precursors which are horizontally overlapping. 
     As used herein, “translation text” refers to the content of the second language which is present as a word, words, a language character, language characters, character set, or character sets. The content of the second language is displayed on the mobile camera device as an augmented reality image text. 
     As used herein, “traditional Chinese characters” means a form of Chinese characters which may contain more strokes and which most foreigners cannot distinguish form simplified Chinese characters. 
     As used herein, “simplified Chinese characters” refers to the form of Chinese characters used by the present invention in the process steps of translation. The present invention converts all the Chinese characters recognized from the first language text that may be traditional Chinese characters into their corresponding simplified Chinese characters to reduce by at least one half the number of Chinese characters that will have to be sorted during the steps of translation. 
     As used herein, “variant conversion” means converting all Chinese characters to simplified Chinese characters before doing the translation. For the present invention, a conversion table was created to halve the size of the dictionary that would have to be searched during the translation of the first language to the second language, with the result that the rate of translation would be doubled. Also, the conversion of traditional Chinese characters to simplified Chinese characters, and then the conversion of the simplified Chinese characters to a second language text can be more accurate than converting both forms of Chinese directly to a second language text. 
     As used herein, “aspect ratio” means the ratio between the height and the horizontal width. The aspect ratio of Chinese characters is usually close to 1, as the characters approximate a square. 
     As used herein, “average character size” can be estimated as the size that the majority of the text characters have before translation to the second language. This size can be estimated in terms of a character&#39;s dimensions (height and horizontal length), and area (height times horizontal length). 
     As used herein, “normalization” relates to the field of image processing, wherein normalization is used to regulate the shape of the image to a fixed size to reduce the variation of images of same class. The normalization process may help to create the same constant dimensions, so that two images under different conditions will have the same characteristic features. 
     As used herein, “feature extraction” means transforming the input data into the set of features. This is useful when the input data to an algorithm is large. Then the input data will be transformed into a reduced representative set of features. The features set can extract the relevant information from the input data and perform satisfactorily in the algorithm of the present invention to detect and isolate various features of a video stream. 
     As used herein, “dimensionality reduction” refers to pattern recognition processing to reduce the number of features to a more manageable number before classification. 
     As used herein, “classification with clustering” means performing several types of agglomerative hierarchical clustering. This process works by finding pairs of clusters to merge by following paths in the classification graph of the clusters until the paths terminate in pairs of mutual similar classes. 
     As used herein, “translation score” refers to a mathematical function that represents a better translation, meaning more terms get translated. 
     Detailed Description of Single-Line Translation Embodiments 
     The drawings merely provide examples of processes for embodiments of the present invention. The example algorithms are directed towards translations processes useful where the first language is Chinese and the translation is into English, but the inventors contemplate the translation between and back and forth between any two languages.  FIGS. 1A and 1B  illustrate a flowchart  150  of an algorithm or process running in video mode to translate a single line of a first language into a second language in accordance with one embodiment of the present invention. In  FIG. 1A , the process  150  begins at step  100 . Process  150  runs in video mode. Each time it finishes the process, the process returns to the top and captures a new frame from the video to execute the process again. This process creates a recognized process string and a corresponding translation appears on the mobile camera device display screen. In step  102 , a decision is performed by the process to determine if the image on the display of the mobile camera device of the present invention is focused. The process allows the camera on the user device to handle the auto-focus functionality. While the camera is focusing, the process checks step  102  repeatedly without doing any processing until the camera stops focusing. Then, the process goes to step  104  to do the processing. Sometimes the camera may have thought it is already focused, so it will process blurred image without trying to focus. Accordingly, the process provides a tap-to-focus functionality for users to force it to re-focus. In step  104 , the process determines if the user has selected a single line of text or multiple lines of text to translate. If the user has selected multiple lines, then the process proceeds to step  106 , wherein the multi-line translation process described in  FIG. 3  is called; otherwise, the process proceeds with the single line translation. 
     In step  108 , the process crops the image from the image processing bounding box selected by the user. To crop the image refers to removal of the outer parts of the image of the first language characters in a bounding box to accentuate the characters. In step  110 , pre-processing occurs for character detection as described below in relation to  FIG. 2A . In step  112 , a determination is made whether or not pre-processing has revealed a text precursor which would indicate there is some preliminary text information suggestive of a text character. If there is no indication for a text precursor in step  110 , the process shows a black box and reset in step  114 , or the like, and returns to starting step  100 . 
     If a text precursor has been identified in step  112 , then the process of step  116  performs character segment recognition as is described in greater detail in  FIG. 2B  below. In step  118 , the process determines if the characters recognized in the previous step are too small. In the case of the process determining that the recognized characters are too small, the process proceeds to step  120 , where a message is displayed to the user, “Try to zoom in or get closer,” or the like, and the process returns back to starting step  100 . If the recognized characters are determined to be large enough in step  118 , then the process proceeds to step  122  in  FIG. 1B . In step  122 , the process filters out non-Chinese characters and proceeds to step  124  where a determination is made as to whether the process string is blank, meaning no language character for translation has been found in the process string and the process proceeds to step  126 , where a message is displayed to the user “Image unclear,” “Use flashlight,” or the like. The process then proceeds from step  126 , back to the beginning of process  150  at starting step  100 . 
     If the determination at step  124  indicates a character has been found, then the process proceeds to step  130 , where the process performs a variant conversion. The variant conversion at step  130  reduces the number of terms in the dictionary by converting any Traditional Chinese characters to Simplified Chinese characters. Converting all Chinese characters to Simplified Chinese characters is performed because sometimes the Chinese text to be translated will be a combination of Simplified and Traditional Chinese characters. Converting Traditional to Simplified is much less complicated than converting Simplified to Traditional. Most foreigners cannot distinguish between Simplified and Traditional Chinese characters. The process of step  130  reduces the size of the Chinese character dictionary needed to be scanned in translation processing of the characters of step  132 . The smaller Chinese-to-English dictionary substantially decreases the amount of processing, and thus increases the processing speed of the single line algorithm in the mobile camera device because the processing and memory capacity can be a processing speed limitation for some mobile camera devices. In step  132 , the process uses results from an optical character recognition (OCR) process for translating the characters of Simplified Chinese to English words. When the translation process in step  132  is completed, the process proceeds to step  134 . 
     In step  134 , the process checks if the image processing bounding box has stayed on the same text in the current string as compared to the previous string. [The process of step  134  checks this by a process of either: a) comparing the similarity of character features in the current string for overlap to character features in the previous process string, or b) a tracking method to check stability of the current image and the previous image.] The process in step  134  calculates if the number of matched characters to number of total characters is high enough to confirm that the bounding box is staying on the same text. The process proceeds from  134  to step  136 , where the current translation is compared to the previous translation. The better translation is saved and the inferior translation is deleted by the process at step  136 . Each time flowchart  150  finishes a process string, the process proceeds back to the start of flowchart  150  and captures a new frame from the video. This process produces a recognized string and a corresponding translation is shown on the display of the mobile camera device. 
       FIG. 2A  illustrates a flowchart for a pre-processing process for character recognition starting at step  202 . In step  202 , a cropped greyscale image has been input from process step  110 . Step  202  proceeds to step  206 , where the cropped greyscale image is up-sampled to a fixed size, and then the process proceeds to step  208 . 
     In step  208 , the process performs a determination of the threshold type in the binarization of the greyscale image. The intensity values of text and background are utilized to determine if the threshold type is a dark background with light precursor characters, or a light background with dark precursor characters. To decide threshold type, the process determines the intensity values of pixels in each row. The process then compares a linear combination of the intensity values to determine the threshold type. After determining the threshold type in the binarization, the process at step  208  then proceeds to adaptive threshold binarization processing to compare intensity values of text and background to control for changes in lighting conditions over the area of the image, for example, those occurring as a result of a strong illumination or shadows. After determining the threshold type in the binarization and the binarization process, the process proceeds to step  210 . 
     For processing in  FIG. 2A  and thereafter, as an alternative embodiment of the present invention, the processing of the cropped image from step  110  could be in a colorscale rather than in a greyscale. The translated words in English on the user interface of the mobile camera device could be presented in a font color selected from the group consisting of a red, an orange, a yellow, a green, a blue, a pink, a purple, and any other color combination(s). 
     In step  210 , a connected component analysis (CCA) is performed to partition the binarized image of the process string into its segments. The connected components have fields of pixels that are either all black or all white. After the process has completed the connected component analysis (CCA), the process proceeds to step  212 . In step  212 , the process de-noises the binarized connected components by removing individual and small clusters of pixels by examining size and shape information of connected components, and then the process proceeds to step  214  which ends the process of flowchart  200  and returns the process string to step  112  in  FIG. 1A . 
       FIG. 2B  illustrates a continuation of flowchart  200  where the process of  FIG. 2B  starts a process of character segment recognition at step  216 . The process proceeds to step  218 , where text precursor information is stored in a memory device. From step  218 , the process then proceeds to step  220  to perform vertical merging by identifying and combining the text precursors that are horizontally overlapping. Horizontally overlapping text precursors are separate text precursors having portions with different vertical coordinates but sharing common horizontal coordinates with respect to a center horizontal line of the image frame. In this case, close but separate text precursors having no overlapping horizontal coordinates will not be processed as sub-components of the same Chinese text character at this stage. After the process of vertical merging the text precursors, the process proceeds to step  222  to exclude the artifact text precursors which are outliers to the apparent single line of text precursors being processed in a single line. 
     In process step  222 , processing is guided by three common properties of a single line of Chinese text. First, a Chinese text character has a square-like aspect ratio at the outer margins of sections of all segments in the character. Secondly, Chinese text characters have a similar vertical height. Thirdly, a single line of Chinese characters is always a proper straight single line of characters and so there will not be a Chinese character higher than another Chinese character in the single line of text. Therefore, the process in step  222  as a single line processes a single line of Chinese text with processing to delete any data for a text precursor outside the region extended from the center horizontal line, where in this region every row overlaps at least one text precursor in the current image string. Accordingly, after the process in step  222  has removed any artifact text precursors outside regions extending from the center horizontal line of the image frame in vertical aspect, the process proceeds to step  224 . In step  224 , the process sorts text characters in a left to right order, and then the process proceeds step  226 , where character size is examined assuming Chinese characters have a square box shape overall. From step  226 , the process proceeds to decision step  228 , where the process determines if the average character size is small. If the process at step  228  determines that the average character size is too small, then the process ends character segment recognition and returns to step  120  of flowchart  150  in  FIG. 1A . If the process at step  228  determines that the average character size is not too small, then the process proceeds to step  230 . 
     In step  230 , the process performs processing with horizontal merging using character recognition feedback on binarized vertically merged text precursors from step  228 . The horizontal merging process starts on the leftmost text precursor in the single line. The process checks the bounding box shape for the text combinations across the text precursors and obtains a shape score for each of them. If an image processing bounding box has an aspect ratio to match the language profile, then the combination is processed in the character recognition feedback process illustrated in  FIG. 2C  (described below) to determine the distance scores of the combinations. The process selects the best combination of the text precursors having the best shape score and distance score, and then excludes this “object” and then repeats the horizontal merging with character recognition feedback processing on the nearest right object until there are no more rightmost objects in the single line image string. If none of the combined shape and distance scores is confident enough to be a character, then just one object is excluded. Many Chinese characters are composed of some other characters, so using shape information helps processing find the most likely character if the character itself and its sub-component distance scores are similar. This also solves the problem if characters in the string are close together and thus hard to segment. This “greedy” algorithm for segmenting a given string reduces computation requirements on mobile devices without having to compute a global optimal solution. When the process of step  230  is completed, the process proceeds to step  232 , where the process is instructed to return to step  122  in  FIG. 1B . 
       FIG. 2C  illustrates a character recognition feedback process which functions as a subroutine that checks the suitability of horizontal merging processed combinations of text precursors, where the combinations of text precursors have been delivered from step  230  of  FIG. 2B . The processing of combinations of text precursors from step  230  of  FIG. 2B  for the character recognition process starts in step  234  illustrated in  FIG. 2C . The binary character recognition process in step  234  proceeds to step  238 . In step  238 , processing determines the regions of interest (ROI) on the binary image of the process string. The region of interest (ROI) on the binary image in step  238  comprises collections of connected components. The process in step  238  proceeds to step  240 , where there is processing to cause image normalization. Normalization of the binary image is a process that regulates the shape of the image in the ROI to a fixed size to reduce the variation of images of same class. When the process of step  240  is completed, the process proceeds to  242  to perform feature extraction processing. The process of feature extraction transforms the input data into a set of features. The input data of the process string which is the normalized image data is very large. Thus, feature extraction is important for reducing the size of the data in subsequent processing steps of the algorithm. After processing to execute feature extraction of normalized region of interest, the character recognition process proceeds to step  244 . In step  244 , the process performs dimensionality reduction. Dimensionality reduction processing is used in step  244  to reduce the number of features to a more manageable number before classification. After dimensionality reduction, the process proceeds to step  246  for classification with clustering processing of the reduced character features. The process of classification with cluster processing causes agglomerative hierarchical clustering that finds pairs of clusters that merge by following paths in the classification graph of the clusters until the paths terminate in pairs of similar classes. Upon completion of step  246 , the process proceeds to process step  248 , which instructs the process string with recognized binary character data to return to step  230  of  FIG. 2B . 
     Detailed Description of Multi-Line Translation Embodiments 
     The present invention provides a multi-line text translation process using tracking and sub-sampled imaging. Multiline text recognition processing requires more computation to process a frame than single-line text recognition processing. To provide real-time user experience, the language translation is overlaid on text as an augmented reality image and a tracking method is used to detect movement of text. The tracking serves two purposes: one is to see if the camera is focusing on the same text. Image character processing is done only if two consecutive steady images are captured. Another purpose of tracking is to have moving direction of text so that text locations can be adjusted accordingly. For real-time tracking, the processing sub-samples the image before processing does tracking. Tracking is performed on the current frame and the previous frame, to obtain vectors with x and y movements. As users will usually focus on text on a flat area, processing determines if a user is holding the phone steadily by checking if the vectors are consistent and small. If the vectors are consistent and small, then character recognition can be performed using a captured video frame or by adjusting the text location on the screen. 
     To make translations overlay on the text in image in real-time, multi-threaded programming is used: text location tracking is done in a one thread while character recognition is done in another thread. The loop of going through tracking is very fast compared to recognition, thus text location can be adjusted in real-time. When the recognition results are ready, the recognition results are extracted, updated on the screen, and updated with text location. If necessary, another recognition in another thread is made. In this update previous results are examined and better results preserved for each text location. 
     The multi-line character recognition method performs: two-way binarization; horizontal blurring; an avoidance of recognition of video frames with unlikely character parts; text cancellation and horizontal line grouping. The multi-line process uses two types of binarization: dark text/bright background and bright text/dark background. Then horizontal blurring processing is used on the binarized images to detect horizontal text. This can be done efficiently and without possibly missing a text location. After horizontal blurring, text recognition is done on these regions. The regions that do not have proper size or aspect ratio are skipped to increase processing speed. Then, text cancellation is done to cancel one of the strings from different types of binarizations that overlap. If two strings overlap, the one is preserved that has more characters. Finally, text grouping is done if characters are separate and apart without being in the same region. Two regions of characters are grouped together according to the interval and location information. 
     Description of the processes of the multi-line process according to the drawings begins here. The decision process at step  104  in  FIG. 1A  sends a first frame of multiple line text for translation to step  106 , where the process sends the first frame of the multi-lines of text to step  302  of  FIG. 3A , where processing of the multi-line text translation process  300  starts. The process at step  302  proceeds to step  304  where the process crops the first frame of the multi-line text to remove the outer parts of the image frame in the image processing bounding box. The process then sends the first cropped frame of the multi-line text to step  306 . In step  306 , the process checks if the cropped frame is the first frame of the multi-lines of text. The cropped frame of multi-line text is the first frame, so the process sends the cropped first frame of multi-line text to step  308 . The process of step  308  saves the first frame of multi-line text in a memory device. The process string at step  308  then returns to step  304 . At step  304  the process crops a second frame of the multi-line text and sends the cropped second frame to step  306 . The process in step  306  determines if the cropped frame is the first frame of the multi-line text. When the current cropped frame at step  306  is not the first cropped frame, and then the process at step  306  sends the cropped first frame and second frame of the multi-line text to step  310 . In decision step  310  the process checks if the pause button on the user interface of the mobile camera device has been pressed. If the pause has not been pressed on the user interface, then the decision process at step  310  sends the first cropped frame and the second cropped frame to step  312 . If the pause button has been pressed on the user interface, then the decision process at step  310  proceeds to step  380  where the process pause processing of the image from step  378  illustrated in  FIG. 3B . 
     At step  312  the process performs resizing of the cropped image for both the previous and current frames before the process performs tracking on the cropped, resized previous frame and current frame. At step  312  the process performs tracking of the current and previous frames. In each of the tracking locations, the process calculates the changes in location of the image from the previous frame to the current frame and the process defines the movement from previous frame to current frame in each tracking location in terms of a vector with X and Y values. The process uses the previous frame as the reference tracking frame and processing ultimately proceeds only a current multi-line of text frame. The process proceeds from step  312  to step  314  where the vector results from the tracking at step  314  are examined. 
     The process proceeds to decision step  316  where the vector results from the tracking are used to prevent processing unstable images with motion blurring. Processing at step  316  first determines (a) whether or not the vectors of the tracking locations are similar. Processing at step  316  also determines (b) whether or not the average vectors of the tracking locations accumulated between recognition result updates are small. In addition, processing at step  316  determines (c) whether or not the current image size matches the image size of the previous frame. When processing at step  316  indicates based on determinations (a-c) that there has been significant movement of current frame relative to the previous frame, then processing at step  316  sends both the current and previous frames of multi-line text to step  318 . At step  318  the process deletes the current and previous frames of the multi-line text from memory and returns the process string to start step  302 . Thus, the current frame is processed forward to step  352  as illustrated in  FIG. 3B  only if the process determines that: (1) the vectors of the tracking algorithm are consistently similar; (2) the average vectors of the tracked locations accumulated between recognition result updates are small; and (3) image size of the current frame matches the image size of the previous frame. When the process at step  316  sends the current frame process string to step  352 , the process at  316  step discards the previous frame. 
     At decision step  352  a determination is made as to whether or not there has been a previous multi-line recognition result at step  368 . If the process determines that there has not been a multi-line recognition result at step  368 , then the process will send the multi-line text image frame for autofocusing at step  356 . The process waits at step  358  until the focusing is completed. The process proceeds to step  366  where the process crops the multi-line text frame to obtain a full resolution image. After step  366 , the process proceeds to step  368  where the focused and cropped multi-line text frame proceeds to a multi-line text recognition process which starts at step  402  in  FIG. 4 , which is described later. When there has been a multi-line recognition result at step  368  then at step  352 , the determination will be that the current frame is not the first frame for multi-line recognition processing, and therefore the current frame will be sent to decision step  354  where the process decides if another thread is running or not. 
     When the process at step  354  determines that the thread count is zero, then processing sends a current frame of the multi-line text image to step  362 . The process in step  362  updates the translation text results displayed on the mobile camera device. Processing in step  362  checks each individual line of text separately in a frame of the multi-line text image; checks to see that text is staying in the bounding box; and checks to see whether previous translation results are better than current translation results and if so uses the previous frame translation results. After process step  362 , the process proceeds to step  372  where the process resets the accumulated small tracking vectors to zero to avoid processing over-accumulation of vector data. The process proceeds to step  374  where the process crops the image frame to obtain a higher resolution image and then processing proceeds to step  376  where the thread is created for multi-line recognition processing as illustrated in  FIG. 4 . In step  378  the current image frame is saved. Note that current frame was cropped in step  304 . 
     In step  354 , when the determination is that the thread count is not zero then the process of step  354  proceeds to step  360  to adjust the text location from the previous frame of the translated text appearing on the image display of the mobile camera device. The adjustment uses the vectors calculation process for comparing the current frame to the previous frame and moves the translated text appearing on the image display of the mobile camera device, wherein the previous frame provided tracking results in step  312 . After step  360 , the process proceeds to step  364  where the shift vectors are accumulated from step  360 . 
     Process pathways for multi-line text image processing from steps  368 ,  364 , and  376  converge at step  378  where the current image frame is saved as a cropped multi-line text image. The process pauses image processing if the pause button has been pressed at step  380 . The pause button is often pressed when a suitable translation has been obtained in order to be able to move the mobile camera device without losing the translation. The process of step  380  then proceeds to step  382  where processing matches the coordinates of the location of the screen with the location of the image buffer (memory) where the processing occurs. The process of steps  368  and  376  illustrated in  FIG. 3B  is a multi-line recognition subroutine process illustrated in  FIG. 4 . in process  400 . 
     In more detail, process  400  begins in step  402 . In step  404  the process performs a first type of binarization where there is a light background and dark text. The process at step  406  then performs recognition processing on the first-type binary multi-line image using either a subroutine process  500  illustrated in  FIG. 5A  which will be described later, or using the subroutine process  550  illustrated in  FIG. 5B  which will be described later. After the processing in process  500  or  550  is completed, the process returns to step  408  in  FIG. 4  where the process performs a second type of binarization where there is a dark background and light text. The process at step  410  then performs recognition processing on the second-type binary multi-line image using either a subroutine process  500  illustrated in  FIG. 5A  or using the subroutine process  550  illustrated in  FIG. 5B . After the processing in process  500  or  550  is completed, the process returns to step  412  in  FIG. 4  where the process performs text cancellation to detect overlapped text using the subroutine process  600  illustrated in  FIGS. 6A and 6B . After the processing in process  600  is completed, the process returns to step  414  in  FIG. 4  where the process performs text grouping for the first type (“Type 1”) binary threshold and text grouping for the second type (“Type 2”) binary threshold. The step  414  is processed in a subroutine process  700  in  FIGS. 7A and 7B  that will be described later. After the text grouping for each type of binary threshold is completed, the process returns to step  416  in  FIG. 4 . The translation process in step  416  translates the Chinese characters, the text of the first language, into English words, the text of the second language, using results from optical character recognition. The output of the translation is displayed on mobile camera device as a real-time augmented reality image. 
     The translation engine in step  416  calculates a translation engine result scores. The translation engine score is high when there are signs that there has been good recognition of Chinese characters and a high probability of a good translation of the Chinese characters into English. After checking to see that the line of text of the current frame with respect to the previous frame is staying and not moving, then the multi-line translation engine score of the line for the current and previous frame translations are compared and when the previous translation engine score is higher, the previous frame translation of the line is retained and the current translation is not saved. The translation engine results are scored by calculating the perceived effectiveness of the translation result and then summing them across the resulting words. The translation process in the multi-line proceeds one line at a time in processing and translation of the multi-line text image. 
     In another embodiment vertical text recognition could be added as a process to the process of horizontal text recognition process. The processing would decide text direction automatically in both vertical and horizontal direction in the first frame recognition. The translation scores would be summed in each direction and the higher translation score would be selected for further processing because the higher score translation would have more meaningful phrases in that direction. Sometimes the process may have low translation scores indicating there are not many characters to be recognized for translation. Some translation scores may be simply noise. Therefore, the process would focus on summing the translation scores of text lines around the center of the image because that is where the user would start focusing. 
     As mentioned above, the process at step  410  then performs recognition processing on the second-type binary multi-line image using either a subroutine process  500  illustrated in  FIG. 5A  or using the subroutine process  550  illustrated in  FIG. 5B . Step  502  in  FIG. 5A  begins a process of recognition processing on one type of the binary multi-line image. This process proceeds to step  504  where the process finds connected components and de-noise the multi-line text image. The process then proceeds to process step  506  where horizontal blurring is performed to find text location. 
     The process then proceeds to step  508  where a connected component analysis is performed on the horizontally blurred image to partition the binarized image of the process string into its segments. The connected components have fields of pixels that are either all black or all white. After the process has completed the connected component analysis, the process proceeds to step  510  where the process performs an optical character recognition (OCR) on the connected components that are located inside each connected component single line region defined by horizontal blurring. Processing in step  510  will not do OCR on a single line region if the binary recognized characters are too small size or when the aspect ratio is unlikely to form a horizontal text line. 
     The alternative pathway  550  for recognition on the binary image multi-line image is illustrated in  FIG. 5B  and starts at step  560 . In step  562  the binary image multi-line image is de-noised. The process proceeds to step  564  where horizontal blurring is performed followed in step  566  by connected component analysis with cropping of the corresponding regions in the original image. The connected component analysis is performed to partition the binarized image of the process string into its segments. The connected components have fields of pixels that are either all black or all white. After the process has completed the connected component analysis, the processing proceeds from step  566  to step  568  where for each connected component region like a single line, the process performs OCR unless the binary recognized characters are too small size or the aspect ratio is unlikely to form a horizontal text line. Then the process resizes each single line of the multi-line text image to a finer resolution and repeats the binarization. The process  550  of  FIG. 5B  has better accuracy than the process of  FIG. 5A  because the images being recognized have better resolution thus contain more details of characters but the processing speed may be slower than the processing in process  500  of  FIG. 5A . 
     As mentioned previously, the process of  FIG. 4  at step  412  performs text cancellation to detect overlapped text using the subroutine process  600  illustrated in  FIG. 6  which has parts  6 A and  6 B. In process  600  of  FIG. 6A , text cancellation after character recognition starts at step  602  and proceeds to step  604  where the process counts the number of characters in Type 1 binarization and in Type 2 binarization. By definition, if the Type 1 binarization consists of black text appearing on a white background, the Type 2 binarization is the opposite binarization, namely white text appearing on a black background. The process of step  604  proceeds to decision step  606  where there is a determination as to whether the processing loop thru the Type 1 binarization is finished. If the processing loop thru Type 1 binarization is finished, then the process string is sent to start text grouping at step  702  at the beginning of process  700  in  FIG. 7A . If the processing loop thru Type 1 binarization is not finished, then the process proceeds to decision step  608  to determine whether there are Chinese characters in a single line of the multi-line text image frame. 
     If step  608  determines there are no Chinese characters in the line at step  608 , then the process string is returned to step  606  to repeat the Type 1 binarization on another single line of the multi-line text image. If step  608  determines there are Chinese characters in a single line of the multi-line text image frame then the process proceeds to  610  to calculate the height and length bounds of the single line. The process in step  610  then proceeds to decision step  612  where the process determines if the processing loop thru the Type 2 binarization is finished. If the processing loop thru the Type 2 binarization is finished at step  612  then the process string returns to step  606  to process another single line of the multi-line text image. If the processing loop thru the Type 2 binarization is not finished at step  612  then the process string proceeds to step  652  in  FIG. 6B . Step  652  is a decision step which determines whether there are Chinese characters in a single line of the multi-line text image frame. If there are no Chinese characters in this single line of the multi-line text image frame, then processing sends the process string to step  612  to repeat the Type 2 binarization on another single line of the multi-line text image frame. If there are Chinese characters in the single line of the multi-line text image frame, then the process proceeds to step  654  to identify the length and height boundaries of the particular single line of the multi-line text image frame. 
     The process of step  654  proceeds to decision step  656  where the step determines whether the strings of the Type 1 and Type 2 binarizations overlap. If the Type 1 and Type 2 binarizations do not overlap then the process returns to the decision step  612  process another single line of the multi-line text thru the Type 2 binarization process. If the Type 1 and Type 2 binarizations do overlap then the process at step  658  removes the string of the type of binarization that has the fewer characters. The process at step  658  uses the character number count for each of the type of binarizations from step  604  in  FIG. 6A  only when the number of characters of the two overlapped text lines are equal. The process of step  658  proceeds to decision step  660  to determine whether the string of the Type 1 binarization or the string of the Type 2 binarization was removed. If the Type 1 binarization was removed, then the process string is sent to decision step  606  to determine if the processing loop thru the Type 1 binarization is finished. If the Type 2 binarization was removed, then the process string is sent to decision step  612  to determine if the processing loop thru the Type 2 binarization is finished. When the strings of Type 1 are all looped through, then text cancelling is completed in  FIGS. 6A and 6B  and the process proceeds to process  700  at step  702  of  FIG. 7A . As mentioned previously, the process of  FIG. 4  at step  414  performs a text grouping process for each binary threshold type and this processing is performed in  FIG. 7  which has parts  7 A and  7 B and is described below. A Chinese text character may be translated to a single English word. Two Chinese text characters or three Chinese text characters may need to be grouped for translation if they are wide apart. The process  700  illustrated in  FIG. 7  determines text grouping on one single text line at a time in each multiline binarized text video image frame. 
     Process  700  of  FIG. 7A  begins at step  702  to start Type 1 binarized image text grouping. Processing in Step  702  proceeds to step  704  where the process counts the number of lines of text in Type 1. Process of step  704  proceeds to decision step  706  which determines whether the processing string has finished the Type 1 text grouping. When Type 1 text groupings has finished at step  706 , then the process string is sent to perform Type 2. If the process at step  706  has not finished Type 1 text grouping, then processing continues to step  708 . 
     In step  708  the process calculates the upper and lower bounds for the vertical range of a single Type 1 text line of the multiline text image frame. The process proceeds to decision step  710  to determine whether text grouping of Type 1 text should be performed. If the process at step  710  determines that text grouping of Type 1 text is not possible, then the process at step  710  returns the process string to step  706  to determine whether the loop thru Type 1 text grouping is finished. If the process at step  710  determines that text grouping of Type 1 text is possible, then the process proceeds to step  712  where the process calculates all of the location information for the Type 1 text characters in a single text line. The process first determines a center point Xi of horizontal position of the Type 1 text line and then the left interval of the two leftmost characters and the right interval of the two rightmost characters of the Type 1 text line. Note that in  FIG. 7A , that the left interval is the distance between text character  716  and text character  718  depicted to right of step  712 . Note that the right interval is the distance between text character  720  and text character  722  depicted to right of step  712 . The process in  712  proceeds to decision step  714  where the process determines if the string process loop thru the remaining lines j is finished. 
     If the step  714  decision process determines the string process loop through the remaining lines j is finished, then the process proceeds to step  752  where processing modifies the location information of line i if grouping text to the right of center point Xi of line “i”. Then the process proceeds from step  752  to step  756  where the processing modifies the location information of line i if grouping text to the left of center point Xi. The process then moves to step  760  to remove text line(s) being grouped and then the process proceeds to decision step  762  where processing at step  762  determines whether text grouping of the current line i occurred. If text grouping of line i did not occur then the text grouping of line i is set in memory as a false possible text grouping. If text grouping of line i did occur then the Type 1 text grouping process string returns to step  710  and since the possible text grouping is still set to true it will proceed to step  712  to find further possibility to group text line(s) into the current line i. (Inventors use the symbols “i” and “j” to indicate the text line we are focusing on. The process examines the possibility to group line “j” into line “i”, so start with line “i” and loop through the remaining lines (“j”) and then go to next line “i” and loop through the remaining lines (“j”) and so on.) 
     If the step  714  decision step determines the string process loop through the remaining lines j is not finished, then the process proceeds to step  754  to calculate center point Xj of line j. (The decision step determines whether the center point Yj of line j is between upper and lower bounds of line i. If it is, then it proceeds to  754 . If it is not, then it returns to  714 .) The process then proceeds to decision step  758  where the process determines whether center point Xj of line J is less than center point Xi of line i. If decision step  758  determines that the center point Xj of line J is less than center point Xi of line I, then process proceeds to step  766  to calculate the left distance between left bound of line i and right bound of line j. 
     If decision step  758  determines that the center point Xj of line j is not less than center point Xi of line i, then process proceeds to step  764  to calculate the right distance between right bound of line i and left bound of line j. 
     After the process at step  766  has calculated the left distance between left bound of line i and right bound of line j. Then the process proceeds to decision step  768  where the process determines whether (1) either line i is a single Chinese character or the left distance is less than twice the left interval of line i; and (2) the left distance between left bound of line i and right bound of line j is less than the minimum stored value. 
     If the process at step  768  determines that the condition of either (1) or (2) above does not hold then the process string returns to step  714  to perform another decision as to whether the process string loop through the remaining lines j is finished. 
     If the process at step  768  determines that both the condition of (1) and (2) above hold then the process proceeds to step  776  to update the minimum stored value of left distance between left bound of line i and right bound of line j. Then the process string from step  776  proceeds to return to step  714  to perform another decision as to whether the process string loop through the remaining lines j is finished. 
     In the case where the decision step  758  determines that Xj is not less than Xi, then the process proceeds to step  764  where the process at step  764  calculates the right distance between right bound of line i and left bound of line j, then the process proceeds to decision step  770  where the process determines whether (3) either line i is a single Chinese character or the right distance is less than twice the right interval of line i; and (4) the right distance between right bound of line i and left bound of line j is less than the minimum stored value. 
     If the process at  770  determines that the condition of either (3) or (4) above does not hold then the process string returns to step  714  to perform another decision as to whether the process string loop through the remaining lines j is finished. 
     If the process at step  770  determines that both the condition of (3) and (4) above hold then the process proceeds to step  774  to update the minimum stored value of right distance between right bound of line i and left bound of line j. Then the process string from step  774  proceeds to return to step  714  to perform another decision as to whether the process string loop through the remaining lines j is finished. 
     When decision step  706  in  FIG. 7A  has determined that Type 1 text groupings has been finished then Type 2 text grouping is performed according to the same kinds of processing steps of process  700  of  FIG. 7A  and  FIG. 7B . However, the processing steps for the Type 2 text grouping are not illustrated specifically in the figures as it would be apparent what they would be to one of skill in the art to which the present invention pertains how to perform the Type 2 text grouping in view of  FIG. 7A  and  FIG. 7B . 
     Detailed Description of User Interface Embodiment and Use Cases 
       FIG. 8  depicts an example of a Chinese food menu. Encircled with label  802  is four lines of Chinese language characters needing a translation into English. Encircled with label  804  is a single line of Chinese language characters needing a translation into English. 
       FIG. 9  depicts a user interface  900  on an example of a mobile camera device that is on an operating process of an embodiment of the present invention. An embodiment of a target box  902  is depicted on the user interface display. The target box can be any size and located anywhere on the mobile camera device. Target box pull down display icon  904  depicted being touched by finger tip  906  which can slide in direction of arrow causes the target box to increase in size. Light illumination switch icon  908  labeled “light” can be tapped to add light illumination during camera focusing on the first language text selected for a translation into a second language. First language text will be displayed in target box. Pause button  910  labeled “freeze” can be tapped to pause or freeze a display of a translation. See  FIG. 10  for example effect on size of target box caused by sliding a finger tip on pull down icon  904 . 
       FIG. 10 . depicts a user interface  1000  on an example of a mobile camera device that is on an operating process of an embodiment of the present invention. An embodiment of a target box here labeled  1002  is depicted on the user interface display. Compared to target box  902  of  FIG. 9 , the size of the target box in  FIG. 10  is larger. Above target box is an instruction “Center text in the box and wait for translation to appear”. Slider icon  1004  has a button and can be slid to zoom the image in the target box as needed. 
       FIG. 11  depicts a user interface  1100  on an example of a mobile camera device that is on an operating process of an embodiment of the present invention. Target box size fits a single line of focused Chinese text characters  1104  for translation by the present invention. These are the same Chinese characters as depicted inside label  804  on Chinese menu depicted in  FIG. 8 . Above the target box is the single line processed image  1102  of Chinese characters. Below the target box is an English translation  1106 . Pull down tab icon is labeled  1108 . Pause icon is labeled  1110 . Light illumination icon  1112  indicates the light is “off.” Second view of user interface depicts a finger on a target box pull down icon  1114 . 
       FIG. 12  depicts a user interface  1200  on an example of a mobile camera device that is on an operating process of an embodiment of the present invention. Target box size fits four lines of focused Chinese text characters shown in very light grey inside the target box. The English translation  1202  is displayed inside the target box in this embodiment of the present invention with each line of Chinese characters and its English translation overlap. In one embodiment, the English translations are on top of the Chinese characters. In one embodiment, the English translation is displayed as an augmented reality image in real-time. 
       FIG. 13 . depicts an expanded view of a user interface  1300 , showing a zoomed-in portion of  FIG. 12 . The target box is labeled  1306  and the pull down icon tab  1312  in this example embodiment of the present invention. The first language text here is Chinese text characters. Their translation into the second language is displayed as English text characters. Each of the four lines of the multi-line translation are in an English text and each line is numbered here, for example lines  1302 ,  1304 ,  1308 , and  1310 . In one embodiment, the English text is in a black font, while the four lines of Chinese text are in a grey (original color) font in accordance with one embodiment of the present invention, but any color may be used for the font color. In other embodiments, the English text is in a white font color. The colors of foreign and translated text may be varied without departing from the spirit or scope of the invention. 
       FIG. 14  depicts an expanded view of a user interface  1400  displaying a single line translation operation by another embodiment of the present invention. A Chinese pronunciation  1406  of the Chinese text characters  1404  is displayed below the target box. Target box size fits a single line of focused Chinese text characters  1404  for translation by the present invention. Above the target box is the single line processed image  1402  of the Chinese characters. Below the target box is an English translation  1408 . 
       FIG. 15  shows various mobile devices  1502 ,  1504 ,  1506 , and  1508  on which the present invention may be practiced. Shown are mobile smartphones  1502 ,  1504 , and  1506 , as well as a wearable computer  1508 , such as, but not limited to, GOOGLE GLASS™. The present invention may be practiced on a variety of mobile and wearable devices, some illustrative examples of which are provided here. However, the applicability of the present invention is by no means limited to the mobile devices or wearable computers shown or described here. It is known that such mobile devices and wearable computers have one or more processors, coupled to one or more memories, which store program code, which may be used to store the program code to execute the processes of the present invention, as shown and described. 
     Japanese Embodiments 
     Due to the fact that there are three different writing systems in Japanese (hiragana, katakana, and kanji), a few characters across these systems can be hard to distinguish on character level, such as (           -         ), (         ), etc. Therefore, the process uses contextual information to distinguish them. The process utilizes a language model and some heuristic rules to achieve higher accuracy. The process can also incorporate shape similarity information of characters along with translation scores to evaluate the most probable string.
     CONCLUSIONS 
     The present invention may be implemented in hardware and/or in software. Many components of the system, for example, network interfaces etc., have not been shown, so as not to obscure the present invention. However, one of ordinary skill in the art would appreciate that the system necessarily includes these components. A user-device is a hardware that includes at least one processor coupled to a memory. The processor may represent one or more processors (e.g., microprocessors), and the memory may represent random access memory (RAM) devices comprising a main storage of the hardware, as well as any supplemental levels of memory e.g., cache memories, non-volatile or back-up memories (e.g. programmable or flash memories), read-only memories, etc. In addition, the memory may be considered to include memory storage physically located elsewhere in the hardware, e.g. any cache memory in the processor, as well as any storage capacity used as a virtual memory, e.g., as stored on a mass storage device. 
     The hardware of a user-device also typically receives a number of inputs and outputs for communicating information externally. For interface with a user, the hardware may include one or more user input devices (e.g., a keyboard, a mouse, a scanner, a microphone, a web camera, etc.) and a display (e.g., a Liquid Crystal Display (LCD) panel). For additional storage, the hardware my also include one or more mass storage devices, e.g., a floppy or other removable disk drive, a hard disk drive, a Direct Access Storage Device (DASD), an optical drive (e.g. a Compact Disk (CD) drive, a Digital Versatile Disk (DVD) drive, etc.) and/or a tape drive, among others. Furthermore, the hardware may include an interface with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), a wireless network, and/or the Internet among others) to permit the communication of information with other computers coupled to the networks. It should be appreciated that the hardware typically includes suitable analog and/or digital interfaces between the processor. 
     The hardware operates under the control of an operating system, and executes various computer software applications, components, programs, codes, libraries, objects, modules, etc. indicated collectively by reference numerals to perform the process techniques described above. 
     In general, the method executed to implement the embodiments of the invention, may be implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions referred to as “computer program(s)” or “computer code(s).” The computer programs typically comprise one or more instructions set at various times in various memory and storage devices in a computer, and that, when read and executed by one or more processors in a computer, cause the computer to perform operations necessary to execute elements involving the various aspects of the invention. Moreover, while the invention has been described in the context of fully functioning computers and computer systems, those skilled in the art will appreciate that the various embodiments of the invention are capable of being distributed as a program product in a variety of forms, and that the invention applies equally regardless of the particular type of machine or computer-readable media used to actually effect the distribution. Examples of computer-readable media include but are not limited to recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks, (DVDs), etc.), and digital and analog communication media, including over wireless media through online stores, sometimes known as “App Stores” for mobile devices. 
     Although the present invention has been described with reference to specific exemplary embodiments, it will be evident that the various modification and changes can be made to these embodiments without departing from the broader spirit of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative sense rather than in a restrictive sense. It will also be apparent to the skilled artisan that the embodiments described above are specific examples of a single broader invention which may have greater scope than any of the singular descriptions taught. There may be many alterations made in the descriptions without departing from the spirit and scope of the present invention.