Abstract:
An image processing system includes an image input section, a first character-extracting section, a second character-extracting section and a synthesizing section. The image input section inputs images which are linked to each other in time series manner. The first character-extracting section extracts a character from a first image input by the image input section. The second character-extracting section extracts a character from a second image input by the image input section. The synthesizing section generates a character string in accordance with the character extracted by the first character-extracting section and the character extracted by the second character-extracting section.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is based on and claims priority under 35 U.S.C. §119 from Japanese Patent Application No. 2006-205795 filed Jul. 28, 2006. 
       BACKGROUND 
       [0002]    1. Technical Field 
         [0003]    The invention relates to an image processing system, a moving image editing processing system, an image processing apparatus, a moving image editing processing apparatus, a computer readable medium storing an image processing program or a moving image editing processing program, and a computer data signal embedded with the image processing program or the moving image editing processing program. 
         [0004]    2. Related Art 
         [0005]    A moving image is stored in a storage medium such as a DVD and a hard disk. Recently, moving image reproducing apparatuses configured to be able to reproduce the moving image from the storage medium using a computer have been developed. Also, a moving image retrieval technique for allowing an operator to easily retrieve an image that the operator wants to view from the moving image has been developed. 
         [0006]    Here, subtitles, telops, etc. which appear in the moving image are detected automatically, and characters such as subtitles are used as indexes. 
       SUMMARY 
       [0007]    According to an aspect of the invention, an image processing system includes an image input section, a first character-extracting section, a second character-extracting section and a synthesizing section. The image input section inputs images which are linked to each other in time series manner. The first character-extracting section extracts a character from a first image input by the image input section. The second character-extracting section extracts a character from a second image input by the image input section. The synthesizing section generates a character string in accordance with the character extracted by the first character-extracting section and the character extracted by the second character-extracting section. 
     
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    Exemplary embodiments of the invention will be described in detail below with reference to the accompanying drawings, wherein: 
           [0009]      FIG. 1  is a conceptual module block diagram of an exemplary embodiment; 
           [0010]      FIG. 2  shows an exemplary flowchart showing a character extracting process according to the exemplary embodiment; 
           [0011]      FIG. 3  is an explanatory view illustrating a specific example (example 1) of the character extracting process according to the exemplary embodiment; 
           [0012]      FIG. 4  is an explanatory view illustrating a specific example (example 2) of the character extracting process according to the exemplary embodiment; 
           [0013]      FIG. 5  is an explanatory view illustrating a specific example (example 3) of the character extracting process according to the exemplary embodiment; 
           [0014]      FIG. 6  is an explanatory view illustrating a specific example (example 4) of the character extracting process according to the exemplary embodiment; 
           [0015]      FIG. 7  is an exemplary flowchart showing a character string generating process according to the exemplary embodiment; 
           [0016]      FIG. 8  is an explanatory view illustrating a specific example (example 1) of the character string generating process according to the exemplary embodiment; 
           [0017]      FIG. 9  is an explanatory view illustrating a specific example (example 2) of the character string generating process according to the exemplary embodiment. 
           [0018]      FIG. 10  is an explanatory view illustrating a specific example (example 3) of the character string generating process according to the exemplary embodiment; 
           [0019]      FIG. 11  is an explanatory view illustrating a specific example (example 5) of the character extracting process according to the exemplary embodiment; 
           [0020]      FIG. 12  is a block diagram showing an exemplary hardware configuration of the exemplary embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0021]    Hereinafter, exemplary embodiments of the invention will be described with reference to the drawings. 
         [0022]    Each drawing shows an exemplary embodiment of the invention.  FIG. 1  shows a conceptual module block diagram of the exemplary embodiment. 
         [0023]    In addition, the term “module” generally means logically separable software, and components such as hardware. Accordingly, the module in the exemplary embodiment means not only a module in a program but a module in a hardware configuration. Therefore, the exemplary embodiment also serves to describe a program, an apparatus, a system, and a method. Further, the module corresponds to its function substantially one to one. However, in implementation, one module may be made up of one program, and plural modules may be made up of one program, or conversely, one module may be made up of plural programs. Further, plural modules may be executed by one computer, and one module may be executed by plural computers in a dispersed or parallel environment. Further, in the following description, the term “connection” includes logical connection in addition to physical connection. 
         [0024]    Further, the term “system” also includes a case where it is realized by one computer besides a configuration in which plural computers, hardware, apparatuses, etc. are connected together over a network, etc. 
         [0025]    The exemplary embodiment has a plural-image input module  110 , a character extracting module  120 , a difference detecting module  130 , a dividing module  140  and a synthesizing module  150 . 
         [0026]    The plural-image input module  110  inputs plural images. Here, the plural images are time-seriously continuous images, and specifically, a moving image. However, the plural images maybe plural time-serially continuously shot still images like panoramic images. In the case of a moving image, each image corresponds to a frame image of the moving image. Here, the time-serially continuous images are plural images between which there is a temporal context. Characters in the time-serially continuous image are at least continuous semantically. For example, in the case of a moving image, the interval between the time-serially continuous images may be a regular time interval. A frame image whose contents change severely (for example, if the moving image is compressed, an image whose compressed volume is large is detected as a frame image) may be selected as a target. Further, the term “frame image” means an image for one screen in the case of displaying a moving image, and means an image among plural images in the case of displaying a still image. 
         [0027]    Further, it is assumed herein that characters, such as subtitles and telops, are present within an image to be input. Further, the characters are not limited to the subtitles, but may be characters captured in an image. Also, the characters are not necessarily limited to ones that can be read well. For example, there is also the case where a color of characters is the same as the color of a background, and thus the characters cannot be read. 
         [0028]    The character extracting module  120  extracts characters from an image input by the plural-image input module  110 . Here, as a technique of character extraction, a text/image separating technique, an MRC (Mixed Raster Content) technique, etc. may be used. In the case where an image input by the plural-image input module  110  is a moving image, images to be processed by the character extracting module  120  are frame images. The character extracting module  120  may extract characters from every frame image. Further, in the case of a moving image, there may be almost no change between frames. Thus, a frame image to be processed may be selected at certain intervals. 
         [0029]    In addition, among the images to be processed by the character extracting module  120 , a time-serially previous image may be referred to as an ““A” image,” and an image that is time-serially subsequent to the “A” image may also be referred to as a ““B” image.” It is not necessary that the “A” image and the “B” image are temporarily adjacent to each other so long as the “A” image and the “B” image have a temporal context. Also, there may be plural images between the “A” image and the “B” image. 
         [0030]    Here, the expression “extracts a character” may include the case where a character is recognized from an image of the extracted character and obtained as text data, in addition to the case where the character is extracted as an image. 
         [0031]    Further, there is also the case where the character extracting module  120  cannot extract characters. As mentioned above, this is, for example, a color of a character is the same as a color of a background, and thus the character cannot be read. 
         [0032]    The difference detecting module  130  detects a difference between a character extracted from the “A” image by the character extracting module  120  and a character extracted from the “B” image by the character extracting module. 
         [0033]    When characters are extracted as images by the character extracting module  120 , the difference detecting module  130  compares the images (pattern matching) to detect a difference as an image. 
         [0034]    Also, when characters extracted by the character extracting module  120  are text data after recognition of the characters, a difference is detected as text data. 
         [0035]    The dividing module  140  divides the difference detected by the difference detecting module  130  in accordance with number of images between the “A” image and the “B” image that are target images of the character extracting module  120 . Here, the case where there is an image between the “A” image and the “B” image includes not only the case where a frame image to be processed is selected at certain intervals as described, but also the case where characters cannot be extracted by the character extracting module  120 . Here, the expression “divides the difference in accordance with the number of images between the “A” image and the “B” image“means that the difference is divided by number obtained by adding 1 to the number of images between the “A” image and the “B” image because the difference includes the “B” image. 
         [0036]    The dividing module  140  may not operate in some cases. This is because when a difference cannot be detected by the difference detecting module  130 , it is not necessary to divide a difference (when characters of the “A” image and characters of the “B” image are the same). 
         [0037]    In other cases, that is, when characters cannot be extracted from images between the “A” image and the “B” image by the character extracting module  120  but a difference is detected by the difference detecting module  130 , this difference is divided in accordance with the number of images between the “A” image and the “B” image. 
         [0038]    The synthesizing module  150  synthesizes portions obtained by dividing the difference characters by the dividing module  140 , with the respective images between the “A” image and the “B” image. 
         [0039]    Further, when characters cannot be extracted from the images between the “A” image and the “B” image by the character extracting module  120  and a difference character between the “A” image and the “B” image is not detected by the difference detecting module  130 , the character extracted from the “A” image or the character extracted from the “B” image is synthesized with the images (images from which characters cannot be extracted) between the “A” image and the “B” image. Further, when a difference is detected by the difference detecting module  130 , a character string is generated and this character string is synthesized with the “B” image on the basis of the character extracted from the “A” image by the character extracting module  120  and the character extracted from the “B” image by the character extracting module  120 . 
         [0040]    Further, the synthesizing module  150  also synthesizes a character string according to the character extracted from the “A” image and the character extracted from the “B” image, in addition to generating the character string. 
         [0041]    Next, an operation of this exemplary embodiment will be described. 
         [0042]    The character extracting process according to the exemplary embodiment will be described with reference to  FIG. 2 . 
         [0043]    In step S 201 , the plural-image input module  110  inputs a moving image. Frame images are input. 
         [0044]    Instep S 202 , the character extracting module  120  extracts a character from each frame image. 
         [0045]    In step S 203 , it is determined as to whether or not the character extracting module  120  extracted a character successfully. At this time, if a character is extracted successfully, the process proceeds to step S 204 . If a character is failed to be extracted, the process returns to step S 202 . That is, step S 202  will be repeated until a character is extracted successfully. As a result, if there is no character in a frame image or a character is failed to be extracted due to a relationship between a color of a character and a color of a background in the frame image, the process is looped. 
         [0046]    In Steps  204 , the difference detecting module  130  acquires a difference between a character extraction result of a current frame image and a character extraction result of a frame image from which a character is extracted successfully prior to the current frame image. 
         [0047]    In step S 205 , the dividing module  140  divides the difference acquired in step S 204  by number of frame images between the current frame image and the frame image from which the character is extracted successfully prior to the current frame image. 
         [0048]    In step S 206 , the synthesizing module  150  re-synthesizes a character string in a frame image with no character that should be extracted, using a character region into which the difference has been divided and a character extraction result of a frame image just previous to the frame image in question. The expression “a frame image with no character that should be extracted” is a frame image for which the answer is set to “No” in step S 203 . 
         [0049]    Then, the process is repeated for all the frame images in the moving image input by the plural-image input module  110  till the completion thereof. That is, the process returns to step S 202 , and the process from step S 202  to step S 206  is performed up to the last frame image. 
         [0050]    A specific example of the character extracting process according to the exemplary embodiment will be described with reference to  FIGS. 3 to 6  and  FIG. 11 . Although the Japanese characters are illustrated in the figures, one skilled in the art would appreciate that the exemplary embodiment shown in the figures can apply to the case where time-seriously continuous images include English characters (e.g., alphabet) or characters in any other language. 
         [0051]      FIG. 3  shows the case where there is a frame image from which a character is extracted successfully, then there are plural continuous frame images from which a character is failed to be extracted, and thereafter there is a frame image from which a character is extracted successfully. 
         [0052]      FIG. 3(A)  shows (six) frame images of a moving image input by the plural-image input module  110 , along temporal axis (t 1  to t 6 ). As shown in  FIG. 3(A) , a frame image at time t 1  is an image from which white a character “o ha yo u” can be read in a black background color. It is noted that Japanese word “o ha yo u” means good morning. Although the frame images from a time t 2  to a time t 5  have white characters “o ha yo u”, these characters cannot be read due to the relationship between the white characters and the background color. A frame image at a time t 6  is an image from which the white characters “o ha yo u” can be read in the black background color like the frame image at the time t 1 . 
         [0053]      FIG. 3(B)  shows results when the character extracting module  120  has extracted characters from the frame images on the temporal axis. In this case, a character image “o ha yo u” ( 301  and  306  in  FIG. 3 ) can be extracted from the frame images at the time t 1  and t 6 . No character image is be extracted from the frame images at the time t 2  to t 5  ( 302 ,  303 ,  304  and  305  in  FIG. 3 ). That is, in the flowchart of  FIG. 2 , results of processes for the frame images at the time t 2  to t 5  become “No” in step S 203 . Process from step S 204  to step S 206  is performed for the frame image at the time t 6 . 
         [0054]    The difference detecting module  130  acquires a difference between a character extraction result of a current frame image (at the time t 6 ) and a character extraction result of a frame image (at the time t 1 ) from which characters are extracted prior to the current frame image. An example of a method for extracting the difference is pattern matching. The character extraction result at the time t 6  is “o ha yo u” ( 306  of  FIG. 3 ). Also, the character extraction result at the time t 1  is “o ha yo u” ( 301  of  FIG. 3 ). Accordingly, it is determined that there is no difference therebetween in this case. 
         [0055]    The dividing module  140  divides the difference in accordance with the number of frame images between the current frame image (at the time t 6 ) and the frame image (at the time t 1 ) from which characters are extracted prior to the current frame image. In this case, the difference will be divided by 5 (5 obtained by adding 1 to the number “4” of frames between the time t 1  and the time t 6 ). However, since no difference is detected, the process of dividing by the dividing module  140  is not performed. 
         [0056]    The synthesizing module  150  synthesizes the result at the time t 1  with a result obtained by dividing the difference, as a character extraction result of the frame image at the time t 2 . In this case, since there is no divided image, the character extraction result of the frame image at the time t 2  is the same as the character extraction result at the time t 1 . Of course, this is the same if the character extraction result at the time t 6  is used. 
         [0057]    The same is true in the cases of the time t 3  to the time t 5 . In this case, the character extraction result at the time t 1  or the time t 6  becomes character extraction results at the time t 3  to the time t 5 , as it is. That is, as shown in  FIG. 3  (A), “o ha yo u” which is an image of the extracted characters is associated with the frame images at the time bases t 2  to t 5  ( 312 ,  313 ,  314 , and  315  of  FIG. 3 ), and is synthesized with each frame image. 
         [0058]      FIG. 4  shows the case where there is a frame image from which a character is extracted, then there are plural continuous frame images from which a character is not extracted, and thereafter, there is a frame image from which a character is extracted. This example is different from the example of  FIG. 3  in that the character extracting module  120  not only extracts a character as an image, but also performs character recognition to handle text data, which is a result of the character recognition, as extracted characters. Further, although there is no difference in  FIG. 3 ,  FIG. 4  shows the case where there is some difference. 
         [0059]      FIG. 4(A)  shows (six) frame images of a moving image input by the plural-image input module  110  according to times bases (t 1  to t 6 ). 
         [0060]    As shown in this figure, a frame image at a time t 1  is an image from which a black character “o” can be read in a white background color. Although frame images from a time t 2  to a time t 5  have black characters “o ha”, “o ha yo”, “o ha yo u”, and “o ha yo u go”, respectively, these characters cannot be read due to the relationship between the black characters and the background color. A frame image at a time t 6  is an image from which black characters “o ha yo u go za” can be read in a white background color. It is noted that the Japanese word “o ha yo u go za i ma su” is a respectful form of “o ha yo u” and means good morning. 
         [0061]      FIG. 4(B)  shows results when the character extracting module  120  has recognized characters from the frame images on the temporal axis. In this case, the character “o” ( 401  of  FIG. 4 ) is recognized from the frame image at the time t 1 . No character is recognized from the frame images at the time t 2  to t 5  ( 402 ,  403 ,  404 , and  405  of  FIG. 4 ). That is, in the flowchart of  FIG. 2 , results of process for the frame images at the time t 2  to t 5  become “No” in Step S 203 . The characters “o ha yo u go za” ( 406  of  FIG. 4 ) is recognized from the frame image at the time t 6 . Process from step S 204  to step S 206  is performed for the frame image at the time t 6 . 
         [0062]    The difference detecting module  130  acquires a difference between a character recognition result of a current frame image (at the time t 6 ) and a character recognition result of a frame image (at the time t 1 ) from which characters are recognized prior to the current frame image. As a method of extracting the difference, for example, comparison between texts is performed. The character recognition result at the time t 6  is “o ha yo u go za” ( 406  of  FIG. 4 ), and the character recognition result at the time t 1  is “o” ( 401  of  FIG. 4 ). Accordingly, the difference in this case is “ha yo u go za” ( 407  of  FIG. 4 ). 
         [0063]    The dividing module  140  divides the difference in accordance with the number of frame images between the current frame image (at the time t 6 ) and the frame image (at the time t 1 ) from which a character is recognized prior to the current frame image. In this case, the difference is divided by 5 (5 obtained by adding 1 to the number “4” of frames between the time t 1  and the time t 6 ). The result of equally dividing the difference into five portions becomes “ha” ( 408  of  FIG. 4 ), “yo” ( 409  of  FIG. 4 ), and “u” ( 410  of  FIG. 4 ), “go” ( 411  of  FIG. 4 ), and “za” ( 412  of  FIG. 4 ). 
         [0064]    The synthesizing module  150  synthesizes the result at the time t 1  (“N”,  421  of  FIG. 4 ) with a result (“ha”,  408  of  FIG. 4 ) obtained by dividing the difference, as a character recognition result of the frame image at the time t 2 . That is, as shown in  FIG. 4(C) , the synthesized result becomes “o ha” ( 422  of  FIG. 4 ). 
         [0065]    The same is true in the cases of the time t 3  to the time t 5 . That is, as shown in  FIG. 4(C) , the synthesized results become “o ha yo” ( 423  of  FIG. 4 ) at the time t 3 , “o ha yo u” ( 424  of  FIG. 4 ) at the time t 4 , “o ha yo u go” ( 425  of  FIG. 4 ) at the time t 5 , and “o ha yo u go za” ( 426  of  FIG. 4 ) at the time t 6 . Then, each of the above characters is synthesized with the frame image. 
         [0066]      FIG. 5  shows the case where there is a frame image from which a character is could be extracted, then there are plural continuous frame images from which characters are be extracted, and thereafter, there is a frame image from which a character is extracted. This example is different from the example of  FIG. 4  in that the character extracting module  120  does not perform character recognition, and extracts characters as an image. 
         [0067]      FIG. 5(A)  shows (six) frame images of a moving image input by the plural-image input module  110  according to time bases (t 1  to t 6 ). As shown in this figure, a frame image at a time t 1  is an image from which a black character “o” can be read in a white background color. Although frame images from a time t 2  to a time t 5  have black characters “o ha,” and “o ha yo”, these characters cannot be read due to the relationship between the black characters and the background color. A frame image at a time t 6  is an image from which black characters “o ha yo u” can be read in a white background color. 
         [0068]      FIG. 5(B)  shows results when the character extracting module  120  extracts characters from the frame images on the temporal axis. In this case, the character “o” ( 501  of  FIG. 5 ) is extracted from the frame image at the time t 1 . No character is extracted from the frame images at the time t 2  to t 5  ( 502 ,  503 ,  504 , and  505  of  FIG. 5 ). That is, in the flowchart of  FIG. 2 , results of the process for the frame images at the time t 2  to t 5  are “No” in step S 203 . The characters “o ha yo u” ( 506  of  FIG. 6 ) is extracted from the frame image at the time t 6 . Process from step S 204  to step S 206  is performed for the frame image at the time t 6 . 
         [0069]    The difference detecting module  130  acquires a difference between a character extraction result of a current frame image (at the time t 6 ) and a character extraction result of a frame image (at the time t 1 ) from which a character is extracted prior to the current frame image. An example of extraction of the difference is a method by pattern matching. The character extraction result at the time t 6  is “o ha yo u” ( 506  of  FIG. 5 ), and the character extraction result at the time t 1  is “o” ( 501  of  FIG. 5 ). Accordingly, the difference in this case becomes “ha yo u” ( 507  of  FIG. 5 ). 
         [0070]    The dividing module  140  divides the difference in accordance with the number of frame images between the current frame image (at the time t 6 ) and the frame image (at the time t 1 ) from which the character is extracted prior to the current frame image. In this case, the difference is divided by 5 (5 obtained by adding 1 to the number “4” of frames between the time t 1  and the time t 6 ). The result of equally dividing the difference into five parts is the left side of “ha” ( 512  of  FIG. 5 ), the right side of “ha” ( 514  of  FIG. 5 ), the left side of “yo” ( 516  of  FIG. 5 ), the right side of “yo” and the left side of “u” ( 518  of  FIG. 5 ), and the right side of “u” ( 520  of  FIG. 5 ). 
         [0071]    The synthesizing module  150  synthesizes the result at the time t 1  (“A”,  511  of  FIG. 5 ) with a result (the left side of “ha”,  512  of  FIG. 4 ) obtained by dividing the difference, as a character recognition result of the frame image at the time t 2 . That is, as shown in  FIG. 5(C) , the synthesized result at the time  2  includes “o” and the left side of “ha” ( 513  of  FIG. 5 ). 
         [0072]    The same is true in the cases of the time t 3  to the time t 5 . That is, as shown in  FIG. 5(C) , the synthesized results is “o ha” ( 515  of  FIG. 5 ) at the time t 3 , “o ha” and the left side of “yo” ( 517  of  FIG. 4 ) at the time t 4 , “o ha yo” and the left side of “u” ( 519  of  FIG. 5 ) at the time t 5 , and “o ha yo u” ( 521  of  FIG. 5 ) at the time t 6 . Then, each of the above character images is synthesized with the frame image. 
         [0073]      FIG. 6  shows the case where characters could be extracted from any frame image, but some of the extracted characters are missing. 
         [0074]      FIG. 6(A)  shows (six) frame images of a moving image input by the plural-image input module  110  according to times bases (t 1  to t 6 ). As shown in this figure, a frame image at a time t 1  is an image from which a black character “o” is read in a white background color. In frame images from a time t 2  to a time t 5 , some of characters are missing due to the black background. That is, in the frame image at the time t 2 , a character “ha” is read, but a character “o” is missing. In the frame image at the time t 3 , characters “o ha” is read, but a character “yo” is missing. In the frame image at the time t 4 , the characters “ha yo u” is read, but the character “o” is missing. In the frame image at the time t 5 , characters “o ha” and “u go” is read, but the character “yo” is missing. A frame image at a time t 6  is an image from which black characters “o ha yo u go za” is read in a white background color. 
         [0075]      FIG. 6(B)  shows results when the character extracting module  120  extracts characters from the frame images on the temporal axis. In this case, the character “o” ( 601  of  FIG. 6 ) is extracted from the frame image at the time t 1 . The character “ha” ( 602  of  FIG. 6 ) is extracted from the frame image at the time t 2 . The characters “o ha” ( 603  of  FIG. 6 ) are extracted from the frame image at the time t 3 . The characters “h yo u” ( 604  of  FIG. 6 ) are extracted from the frame image at the time t 4 . The characters “o ha u go” ( 605  of  FIG. 6 ) are extracted from the frame image at the time t 5 . The characters “o ha yo u go za” ( 606  of  FIG. 6 ) are extracted from the frame image at the time t 6 . That is, in the flowchart of  FIG. 2 , the process from Step S 204  to Step  206  is performed for the frame images at the time bases t 2  to t 6 . 
         [0076]    Process at the-time t 2  will be described. 
         [0077]    In Step  204 , the difference detecting module  130  acquires a difference between a character extraction result of a current frame image (at the time t 2 ) and a character extraction result of a frame image (at the time t 1 ) from which the character is be extracted prior to the current frame image. For example, as a result of pattern matching, it is detected that there is a difference between “o” ( 611  of  FIG. 6 ) and “ha” ( 602  of  FIG. 6 ). 
         [0078]    In step S 205 , the dividing module  140  has a division number “1,” and divides the difference by 1. Further, it is not necessary to perform this process since the number of divisions is “1. ” 
         [0079]    In step S 206 , the synthesizing module  150  generates a character string according to time points when the characters appear, and appearance locations where the characters appear in the frame images. That is, in this case, although the both characters “o” and “ha” have the same appearance location, the time point of appearance of “o” is earlier than that of “ha.” The generated character image is an image obtained by adding “ha” behind “o.” In addition, “o ha” ( 612  of  FIG. 6 ) is stored as a character extraction result of the current frame image (at the time t 2 ). The generation positional information is recalculated in consideration of positional information on the following frame image, and the size of a character region of the preceding frame image. In this case, the positional information shifts by “o” of the previous frame image from the position of “ha” of the following frame image. 
         [0080]    Next, process at the time t 3  will be described. 
         [0081]    In Step  204 , the difference detecting module  130  acquires a difference between a character extraction result of a current frame image (at the time t 3 ) and a character extraction result of a frame image (at the time t 2 ) from which characters are extracted prior to the current frame image. For example, as a result of pattern matching, it is detected that there is no difference between “o ha” ( 612  of  FIG. 6 ) and “o ha” ( 603  of  FIG. 6 ). 
         [0082]    In step S 205 , the dividing module  140  has a division number “1,” and divides the difference by 1. It is not necessary to perform any process since the number of divisions is “1.” 
         [0083]    In step S 206 , the synthesizing module  150  stores a character extraction result of the current frame image (at the time t 3 ). A stored character string is “o ha” ( 613  of  FIG. 6 ). Further, the stored positional information is the positional information of the current frame image (at the time t 3 ). 
         [0084]    Next, process at the time t 4  will be described. 
         [0085]    In step  204 , the difference detecting module  130  acquires a difference between a character extraction result of a current frame image (at the time t 4 ) and a character extraction result of a frame image (at the time t 3 ) from which characters are extracted prior to the current frame image. For example, as a result of pattern matching, it is detected that there is a difference between “o ha” ( 613  of  FIG. 6 ) and “ha yo u” ( 604  of  FIG. 6 ). 
         [0086]    In step S 205 , the dividing module  140  has a division number “1,” and divides the difference by 1. It is not necessary to perform any process since the number of divisions is “1.” 
         [0087]    In step S 206 , the synthesizing module  150  calculates a logical sum of “o ha” and “ha yo u” with using the pattern-matched character (“o” in this case) as a core. This generates “o ha yo u” ( 614  of  FIG. 6 ). In addition, “o ha yo u” ( 614  of  FIG. 6 ) is stored as a character extraction result of the current frame image (at the time t 4 ). Further, the positional information is recalculated in consideration of the size of a character region of the previous frame image (at the time t 3 ) with a character pattern-matched with using the positional information on the current frame image (at the time t 4 ) as a core. In this case, the positional information shifts by “o” with using “ha” (pattern-matched character) of the following frame image as a center. Then, the generated character string is synthesized with a position indicated by the positional information acquired prior to the frame image at the time t 4 . 
         [0088]    Next, process at the time t 5  will be described. 
         [0089]    In step  204 , the difference detecting module  130  acquires a difference between a character extraction result of a current frame image (at the time t 5 ) and a character extraction result of a frame image (at the time t 4 ) from which characters are extracted prior to the current frame image. For example, as a result of pattern matching, it is detected that there is a difference between “o ha yo u” ( 614  of  FIG. 6 ) and “o ha u go” ( 605  of  FIG. 6 ). 
         [0090]    In Step S 205 , the dividing module  140  has a division number “1,” and divides the difference by 1. It is not necessary to perform any process since the number of divisions is “1.” 
         [0091]    In step S 206 , the synthesizing module  150  calculates a logical sum of “o ha yo u” and “o ha u go” with using the pattern-matched character(s) (“o ha” or “u” in this case) as a core. This generates “o ha yo u go” ( 615  of  FIG. 6 ). In addition, “o ha you go” ( 615  of  FIG. 6 ) is stored as a character extraction result of the current frame image (at the time t 5 ). Further, the positional information is recalculated in consideration of the size of a character region of the previous frame image (at the time t 4 ) with a character pattern-matched with using the positional information on the current frame image (at the time t 5 ) as a core. In this case, since “o ha” (matched characters) of the current frame image is at the utmost ends of all the current frames, they are used as positional information as they are. Then, the generated character string is synthesized with a position indicated by the positional information acquired prior to the frame image at the time t 5 . 
         [0092]    Next, processing at the time t 6  will be described. 
         [0093]    In step  204 , the difference detecting module  130  acquires a difference between a character extraction result of a current frame image (at the time t 6 ) and a character extraction result of a frame image (at the time t 5 ) from which characters are extracted prior to the current frame image. For example, as a result of pattern matching, it is detected that there is a difference between “o ha yo u go” ( 615  of  FIG. 6 ) and “o ha yo u go za” ( 606  of  FIG. 6 ). 
         [0094]    In step S 205 , the dividing module  140  has a division number “1,” and divides the difference by 1. It is not necessary to perform any process since the number of divisions is “1.” 
         [0095]    In step S 206 , the synthesizing module  150  does not need to perform synthesizing since the character extraction result at the time t 6  includes the character extraction result at the time t 5 , and the character position of the difference is a right end. In addition, “o ha yo u go za” ( 616  of  FIG. 6 ) is stored as a character extraction result of the current frame image (at the time t 6 ). The stored positional information is also the positional information in the current frame image. 
         [0096]    It is also possible to perform a portion of the process (flowchart shown in  FIG. 2 ) in this exemplary embodiment as follows. That is, the process shown in a flowchart of  FIG. 7  may be added after Step S 207  of  FIG. 2 . 
         [0097]    In Step S 701 , the difference between a character extraction result of a current frame image and a character extraction result of the previous frame image is acquired. 
         [0098]    In Step S 702 , the difference in Step S 701  is added to generate a character string. 
         [0099]    These steps are repeated until any target frame image is not left. 
         [0100]    Specific examples of the process will be given below.  FIG. 8  shows the case where a character string is displayed in units of row. An example of such a case includes the case where a character string is displayed in units of row from the bottom of a screen. 
         [0101]    The character extraction results, as shown in  FIG. 8(A)  are “Good morning.” at the time t 1 , “Good morning.” at the time t 2 , “Good morning.” at the time t 3 , “It&#39;s fine today.” at the time t 4 , “It&#39;s fine today.” at the time t 5 , and “It&#39;s fine today.” at the time t 6 . 
         [0102]    It is determined in step S 701  of the time t 2  that there is no difference between the time t 1  and the time t 2 , and there is no character to be added in step S 702 . Accordingly, the extracted character string is “Good morning.” as shown in  FIG. 8(B) . 
         [0103]    The same is true in the case of the time t 3 . That is, the extracted character string at this time point is “Good morning” as shown in  FIG. 8(B) . 
         [0104]    A difference is detected in step S 701  of the time t 4 . The difference is added in step S 702 . That is, the generated character string, as shown in  FIG. 8  (C), is “Good morning. It&#39;s fine today.” 
         [0105]    Since no difference is detected at the time t 5  and t 6 , the generated character string is “Good morning. It&#39;s fine today.” 
         [0106]      FIG. 9  shows the case where characters are displayed with another character being added one by one. An example of such a case includes the case where characters are displayed one by one from the right of a screen, and the previous characters move to the left. 
         [0107]    The character extraction results, as shown in  FIG. 9(A) , “o” at the time t 1 , “o ha” at the time t 2 , “o ha yo” at the time t 3 , “o ha yo u” at the time t 4 , “o ha yo u go” at the time t 5 , “ha yo u go za” at the time t 6 , “yo u go za i” at the time t 7 . That is, since the maximum number of characters which can be extracted at every time point is five, “o ha yo u go za i” cannot be extracted from one frame image. 
         [0108]    In step S 701 , a difference is detected at each time point. 
         [0109]    In step S 702 , the rightmost character that is a difference is added on the basis of the positional relationship within a frame image. Specifically, at the time t 2 , a difference between the time t 1  and the time  2  is detected in step S 701 . Thus, the difference “ha” is added to the last of a character string, to thereby generate “o ha”. 
         [0110]    At the following time points, the same process is performed, to thereby generate a character string “o ha yo u go za i.” 
         [0111]    In particular, after extracted character strings are compared with each other, only a difference therebetween may be added. For example, “o ha yo u” and “o ha yo u go” are extracted at the time t 4  and the time t 5 , respectively. In this case, only the difference “go” is added to the last of “o ha yo u” extracted at the time t 4 , to thereby generate “o ha yo u go”. 
         [0112]    Also, In the case where a character is added to an image from the right end thereof and one character is added and displayed in one frame, a simpler method may be adopted. The simple method will be described with reference to  FIG. 10(F)   FIG. 10  also shows the case where the same characters as those of  FIG. 9  are displayed with another character being added one by one. 
         [0113]    Character extraction results shown in  FIG. 10(A)  are the same as those of  FIG. 9(A) . 
         [0114]    By adding only one character at the right end that is the last character of the extracted characters, a character string is generated. Specifically, the character at the right end of the time t 2  is “ha.” This character is added to the end of a character string “o” ( FIG. 10(B) ) generated at the previous time t 1 , to thereby generate “o ha” ( FIG. 10(C) ). By repeating this process, as shown in  FIG. 10(H) , the character string “o ha yo u go za i” is finally generated. 
         [0115]    The case where characters are in a captured image will be described with reference to  FIG. 11 . 
         [0116]    An image which is to be processed by this exemplary embodiment does not necessarily need to be synthesized with characters, such as subtitles or telops, which are moving in a moving image. When a video camera is moved to capture characters on a signboard, etc., this situation is the same as the case where characters are moving. 
         [0117]    Characters extracted from a moving image may be adopted when photographing conditions are good, like the case where all characters are captured in one frame image. However, this exemplary embodiment is useful even in the cases where characters become invisible as follows. 
         [0118]    For example, there may be the followings. 
         [0119]    The case where characters are moved and displayed in an electrical bulletin board, and missing of a character occurs without emitting light only in some portion of the electrical bulletin board. 
         [0120]    The case where missing of a character occurs due to obstructions (a person, an automobile, etc.) during photographing. 
         [0121]    The case where characters are hardly seen due to automatic backlight correction by a video camera, etc. 
         [0122]    The case where missing of a character occurs due to the performance of character extraction and character recognition. 
         [0123]    Here, a specific example will be described. 
         [0124]    For example, it is assumed that a video camera captures images of a signboard displaying “7th OXΔ kindergarten show-and-tell” while being moved to the right (see  FIGS. 11(A) and 11(B) ). 
         [0125]    In this case, it is assumed that images from a frame  1  to a frame  5  are captured as shown in  FIG. 11(C) . Then, a character string “7th OXΔ” is extracted in the frame  1 , a character string “OX kindarga” is extracted in a frame  2 , a character string “dargarten sho” is extracted in a frame  3 , a character string “en show-and-” is extracted in a frame  4 , a character string “-and-tell” is extracted in the frame  5 . However, since a person stands in front of the signboard in the frame  2 , a character “Δ” which should be extracted is hidden. Even in such a case, the character string “7th OXΔ kindergarten show-and-tell” can be generated by the aforementioned processing of the exemplary embodiment. 
         [0126]    With reference to  FIG. 12 , an exemplary hardware configuration of an image processing system according to the embodiment will be described. A configuration shown in FIG.  12  is an image processing system constituted by, for example, a personal computer (PC), etc. 
         [0127]    A CPU (Central Processing Unit)  401  is a control unit which executes processing according to computer programs which describe execution sequences of various kinds of modules described in the above-mentioned embodiment, i.e., the character extracting module  120 , the difference detecting module  130 , the dividing module  140 , and the synthesizing module  150 . 
         [0128]    A ROM (Read-Only Memory)  402  stores programs, operation parameters, etc. to be used by the CPU  401 . A RAM (Random Access Memory)  403  stores a program to be used during execution of the CPU  401 , parameters (for example, results during character extraction) that appropriately vary during execution of the program, etc. These are connected with one another by a host bus  404  constituted by CPU paths, etc. 
         [0129]    The host bus  404  is connected to an external bus  406 , such as a PCI (Peripheral Component Interconnect/Interface) bus via a bridge  405 . 
         [0130]    A keyboard  408 , and a pointing device  409 , such as a mouse, are input devices operated by an operator. A display  410  is composed of a liquid crystal display or CRT (Cathode Ray Tube), and displays a moving image or various kinds of information (text, image information, etc.) that are targets to be processed in the exemplary embodiment. 
         [0131]    A HDD (Hard Disk Drive)  411 , which has a hard disk built therein, drives the hard disk, and records or reproduces programs to be executed by the CPU  401 , moving images, various kinds of information. Images input by the plural-image input module  110 , result data by the character extracting module  120 , etc. are stored in the hard disk. Moreover, other various computer programs, such as various data processing programs, are stored in the hard disk. 
         [0132]    A drive  412  reads data or programs recorded on a mounted removable recording medium  413 , such as a magnetic disc, an optical disk, a magneto-optic disk, or semiconductor memory, and supplies the data or program to the RAM  403  connected via the interface  407 , the external bus  406 , the bridge  405 , and the host bus  404 . The removable recording medium  413  can also be used as the same data storage area as the hard disk. 
         [0133]    A connection port  414 , which is a port which connects with an external connecting apparatus  415 , has connections, such as USB and IEEE 1394. The connection port  414  is connected to the CPU  401 , etc via the interface  407 , the external bus  406 , the bridge  405 , the host path  404 , etc. A communication unit  416  is executed to a network to execute data communication processing with the outside. 
         [0134]    In addition, the hardware configuration of the image processing system shown in  FIG. 12  is an exemplary configuration, and the image processing system of the exemplary embodiment is not limited to the configuration shown in  FIG. 12 . Any configurations may be adopted if only they can execute the modules described in the exemplary embodiment. For example, some modules may be constituted by dedicated hardware (for example, ASIC etc.), and some modules may be located within an external system and be connected by a communication line. Moreover, a plurality of the systems shown in  FIG. 12  may be connected with one another by a communication line so that they may cooperate with one another. Further, the system may be assembled into video cassette recorders, video cameras, digital cameras, information appliances, cellular phones, game machines, GPS, etc. 
         [0135]    Although the exemplary embodiment shows that characters are synthesized with an image by the synthesizing module  150 , characters may be given as an index of the image. That is, differences characters divided by the dividing module  140  may be associated with images between the “A” image and the “B” image as indexes. Here, the indexes are ones that are used when a moving image is edited or retrieved. Indexes may be given to all frames, respectively, and/or an index may be given to every scene. Further, in the case where extracted characters are text data, whenever the characters become a character string that forms one block semantically through morphological analysis thereof, they may be given to a frame image as an index. By doing so, convenience, such as editing and retrieval, is further enhanced. That is, the indexes can be utilized for fast forwarding or scene skipping. 
         [0136]    Further, other methods of utilizing extracted character strings include the following ones. 
         [0137]    (1) The character strings are utilized for the titles of videotapes. 
         [0138]    (2) The character strings are used as keywords when a desired moving image is retrieved from a moving image database. 
         [0139]    (3) The character strings are used as keywords when a plurality of moving images are sorted by category. 
         [0140]    (4) The character strings can also be utilized for cellular phones, GPS, etc. if targets are restaurants, ground names, etc. 
         [0141]    In the embodiment, only one character string has appeared in one frame image. However, for example, even when a row of upper character string and a row of lower character string exist simultaneously, it is possible to synthesize the character strings independently from each other from the positional relationship thereof, pattern matching of extracted character strings, etc. 
         [0142]    In addition, the above-described programs can be stored in a recording medium, or the programs can be provided by communication means. In that case, for example, the above-described programs can also be grasped as inventions of “computer-readable recording media on which programs are recorded.” 
         [0143]    The expression “computer-readable recording media on which programs are recorded” means recording media on which programs that can be read by a computer are recorded, and which are used for installation or execution of programs, distribution of programs, etc. 
         [0144]    In addition, the recording media includes, for example, digital versatile disks (DVDs), such as DVD-Rs, DVD-RWs, and DVDLRAMs, which are standards formulated in a DVD forum, and such as DVD+Rs and DVD+RW, which are standards formulated in a DVD+RW party, compact disks (CD), such as read-only memories (CD-ROMs), CD-recordables (CD-Rs), and CD-rewritables (CD-RWs), magneto-optic disks (MOs), flexible disks (FDs), magnetic tapes, hard disks, read-only memories (ROMs), electrically erasable programmable read-only memories (EEPROMs), flash memories, random access memories (RAMs), etc. 
         [0145]    Also, the above programs or some of the programs can be saved or distributed in a state where they are recorded on the above recording media. Further, the above programs can be transmitted by communication, for example, using transmission media, such as local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), and wired networks, wireless communication networks, or combined networks thereof which are used for the Internet, intranets, extranets, etc. Moreover, the above programs can also be delivered on a carrier. 
         [0146]    Furthermore, the above programs may be portions of other programs, or may be recorded on recording mediums along with a separate program.