Patent Publication Number: US-11393361-B1

Title: Braille reader system using deep learning framework

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority to U.S. Provisional Application No. 63/288,224, filed Dec. 10, 2021, the entire contents which is incorporated by reference herein in its entirety for all purposes. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure is directed to a Braille reader system including a deep learning framework which converts printed Braille dots to speech. 
     Description of Related Art 
     The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention. 
     Braille is a form of written language used by visually impaired persons to read a document consisting of raised dots. In Braille, characters or words are represented by patterns of raised dots that are felt with the fingertips. Braille script is comprised of Braille cells, each representing a character or a word. A Braille sheet is a paper embossed with raised dots that are arranged in Braille cells. A Braille cell is represented by a collection of 6 raised dots arranged in two columns of 3 dots each. Braille script may be presented on Braille paper, on a refreshable Braille display device with dots or round-tipped pins raised through holes in a flat surface, by a Braille input device, and on public notices and signboards. 
     Various computer vision and image processing techniques may be used to recognize the characters or words from scanned documents and images of Braille script and convert the recognized characters or words to speech. Such techniques may assist a visually disabled person, an instructor or trainer in reading the Braille scripts. 
     Various solutions have been developed in recent years for the recognition of Braille scripts. A contact type image sensor that can be worn on a finger and configured to superimpose multiple images obtained from the contact type image sensor to obtain a composited image, convert the composited image into a Braille pattern, and convert the Braille pattern into a corresponding voice was described in JP2011070530A, “Contact type image sensor and image recognition device”, incorporated herein by reference in its entirety. However, this reference has a drawback that the contact type image sensor comes in contact with the Braille pattern instead of the finger of the user. This drawback may hinder the learning experience of the blind person, as the tactile feedback from touching the Braille dots serves to orient the hand towards the next Braille word. 
     A mobile communication terminal to convert Braille points into voice by taking a picture of Braille points through a camera and recognize the Braille image to output the Braille image as a voice was described in KR2007057351A, “Mobile communication terminal for converting Braille points into a voice, especially concerned with recognizing the Braille points to output the recognized Braille points as the voice”, incorporated herein by reference in its entirety. However, it is difficult for a blind person to take a picture of the Braille pattern with a camera, as the individual cannot orient the camera without vision. 
     A semantic segmentation model trained by using a convolutional neural network and Braille images are input into the semantic segmentation model which obtains a semantic segmentation result diagram of the Braille image was described in CN110298236A, incorporated herein by reference in its entirety. However, this reference outputs images of the Braille pattern which are not readable by a blind person. 
     Further, a deep convolutional neural network (DCNN) model that takes pre-processed Braille images as input to recognize Braille cells has been proposed. (See: Abdulmalik Alsalman, Amani Alsalman, Abdu Gumaei, and Suheer Ali Al-Hadhrami, “A Deep Learning-Based Recognition Approach for the Conversion of Multilingual Braille Images”, Article in Computers, Materials and Continua, March 2021, DOI: 10.32604/cmc.2021.015614, incorporated herein by reference in its entirety). However, this reference does not convert the Braille pattern to speech so is not useful for a blind person. 
     Each of the aforementioned references suffers from one or more drawbacks hindering their adoption. Accordingly, it is one object of the present disclosure to provide a Braille reader system to capture and process Braille images for real time recognition of characters and words corresponding to Braille cells sensed by a finger of a user, and outputs speech. 
     SUMMARY 
     In an exemplary embodiment, a system for converting printed Braille dots to speech is disclosed. The system includes a 3D ring case and a digital camera mounted in the 3D ring case. The digital camera is configured to capture a Braille image of the printed Braille dots. The system further includes a rechargeable battery and a speaker. The system further includes a microprocessor operatively connected to the rechargeable battery, the digital camera, and the speaker. The microprocessor is configured to perform data processing and one or more image recognition operations which match the Braille image to a textural character corresponding to the Braille image. The microprocessor is further configured to convert the textural character to an audio waveform and transmit the audio waveform to the speaker. The speaker is configured to receive the audio waveform and generate a sound representative of a spoken word corresponding to the textural character. 
     In another exemplary embodiment, a method for converting printed Braille dots to speech is disclosed. The method includes capturing a Braille image of the printed Braille dots. The method further includes matching the Braille image to a textural character corresponding to the Braille image. Data processing and one or more image recognition operations are performed to match the Braille image to the textural character. The method further includes converting the textural character to an audio waveform and transmitting the audio waveform to a speaker. The method further includes generating, by the speaker, a sound representative of a spoken word corresponding to the textural character. 
     In another exemplary embodiment, a method for converting printed Braille dots to speech is disclosed. The method includes directing a lens of a digital camera towards Braille text including printed Braille dots. The digital camera is confined in a 3D ring case. The method further includes compressing a push button to capture a Braille image of the printed Braille dots. The method further includes matching the Braille image to a textural character corresponding to the Braille image. A microprocessor is configured to perform data processing and one or more image recognition operations to match the Braille image to the textural character. The method further includes converting the textural character to an audio waveform and transmitting the audio waveform to a speaker. The method further includes generating, by the speaker, a sound representative of a spoken word corresponding to the textural character. 
     The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure, and are not restrictive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete appreciation of this disclosure and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein: 
         FIG. 1  is a schematic diagram of a Braille reading device, according to certain embodiments. 
         FIG. 2  is a schematic diagram of a 3D ring case, according to certain embodiments. 
         FIG. 3A  is a schematic diagram of a microprocessor housing, where (a) is a base and (b) is a lid of the microprocessor housing, according to certain embodiments. 
         FIG. 3B  is a schematic diagram of a microprocessor housing, according to certain embodiments. 
         FIG. 4  is a process flow diagram illustrating a conversion of printed Braille dots to speech, according to certain embodiments. 
         FIG. 5  is an exemplary process flow of a method for converting printed Braille dots to speech, according to certain embodiments. 
         FIG. 6  is an exemplary process flow of a method for converting printed Braille dots to speech, according to certain embodiments. 
         FIG. 7  is an illustration of a non-limiting example of details of computing hardware used in the computing system, according to certain embodiments. 
         FIG. 8  is an exemplary schematic diagram of a data processing system used within the computing system, according to certain embodiments. 
         FIG. 9  is an exemplary schematic diagram of a processor used with the computing system, according to certain embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     In the drawings, like reference numerals designate identical or corresponding parts throughout the several views. Further, as used herein, the words “a,” “an” and the like generally carry a meaning of “one or more,” unless stated otherwise. 
     Furthermore, the terms “approximately,” “approximate,” “about,” and similar terms generally refer to ranges that include the identified value within a margin of 20%, 10%, or preferably 5%, and any values therebetween. 
     Aspects of this disclosure are directed to a system, device, and method for converting printed Braille dots to speech. The present disclosure discloses a device to be worn by a user. The device includes a camera to capture images of printed Braille dots. The device includes a microprocessor that processes the captured images of printed Braille dots to determine textural characters or words corresponding to the images of printed Braille dots. 
       FIG. 1  depicts a schematic diagram of a Braille reading device  100  for reading Braille scripts, according to exemplary aspects of the present disclosure. 
     According to aspects of the present disclosure, the Braille reading device  100  includes a three-dimensional (3D) ring case  102 , a microprocessor housing  104 , a digital camera  106 , a microprocessor board  108 , a switch  110 , a flexible flat cable (FFC)  112 , a speaker  114 , a strap  115 , and a pair of connecting wires  116 . 
     Referring to  FIG. 2 , the 3D ring case  102  includes a first ring  203  and a second ring  204 . The second ring is positioned below the first ring. The digital camera  106  is arranged in the first ring. The 3D ring case  102  is worn on a finger of a user by inserting the finger of the user in the second ring. The switch  110  is arranged on the 3D ring case  102 . The 3D ring case  102  is explained in further detail with reference to  FIG. 2 . 
     Referring to  FIG. 1  and  FIG. 3A , the microprocessor housing  104  may include a base  303  and a lid  305 . The base holds the microprocessor board  108 . The lid may be placed on top of the base to enclose components arranged on the base. A top side of the lid includes a cut-out portion to receive the FFC  112 . The microprocessor board  108 , enclosed in the microprocessor housing  104 , is connected to the digital camera  106  by the FFC  112  passing through the cut-out portion. The base includes the strap  115  (shown as  315 ,  FIG. 3B ) for the user to wear the microprocessor housing  104  on the wrist of the user. The lid further includes another cut-out portion on a sidewall (or a lateral side) to hold the speaker  114 . In an aspect of the present disclosure, the speaker  114  may be arranged within the microprocessor housing  104 . The microprocessor housing  104  is explained in further detail with reference to  FIG. 3A  and  FIG. 3B . 
     Referring back to  FIG. 1 , the Braille reading device  100  further includes a battery (not shown) to power one or more of the digital camera  106 , the microprocessor board  108 , and the speaker  114 . The battery may be arranged in the microprocessor housing  104 . In an aspect of the present disclosure, the battery may be a rechargeable battery. The microprocessor housing  104  may include a power interface that may be connected to a power charging adapter to charge the rechargeable battery. In an aspect of the present disclosure, the battery may be replaced with a new battery when the battery runs out of power. 
     In an aspect of the present disclosure, the speaker  114  may be an external speaker or a headphone that may connect to the microprocessor board  108  via one or more audio interfaces. The one or more audio interfaces may include a wired audio interface or a wireless audio interface. The wired audio interface may include high definition multimedia interface (HDMI), a 3.5 mm audio jack, and a universal serial bus (USB) interface. The wireless audio interface may include Bluetooth or Wireless Fidelity (Wi-Fi) connection. The speaker  114  may be powered by the battery of the Braille reading device  100 , by an in-built battery in the speaker  114 , or an external AC or DC power supply. 
     The pair of connecting wires  116  connects the switch  110  to the microprocessor board  108 . The pair of connecting wires  116  passes through the cut-out portion on the top side of the lid to connect the switch  110  to the microprocessor board  108  enclosed in the microprocessor housing  104 . In an aspect of the present disclosure, the microprocessor board  108  may include General-Purpose Input/Output (GPIO) pins. The GPIO pins provide a physical interface between the microprocessor board  108  and external components. One end of the pair of connecting wires  116  is connected to the switch  110 , and the other end of the pair of connecting wires  116  is connected to the GPIO pins. The switch  110  activates the Braille reading device  100 . 
     In operation, the user may wear the 3D ring case  102  on a finger of either hand and wear the microprocessor housing  104  on the wrist of the same hand. To read or learn Braille script, the user may scan the Braille script by sensing printed Braille dots with the finger. The user may press the switch  110  to activate the Braille reading device  100  to identify one or more characters or words corresponding to the printed Braille dots sensed by the finger. When the user presses the switch  110 , the digital camera  106  captures a Braille image of the Braille dots. The Braille image is transmitted to the microprocessor board  108  over the FFC  112 . The microprocessor board  108  comprises a microprocessor that performs one or more image processing techniques or algorithms to process the Braille image for image recognition. Further, the microprocessor may input the processed Braille image to a trained deep learning based convolutional neural network (CNN) model. The trained deep learning based CNN model is trained on a dataset of images of Braille scripts. The trained deep learning based CNN model accepts the processed Braille image, extract features from the processed Braille image, and classifies them into respective classes to recognize one or more characters or words corresponding to the Braille image. The microprocessor generates an audio waveform corresponding to the recognized characters or words and transmits the audio waveform to the speaker  114 . The speaker  114  outputs the sound corresponding to the audio waveform. 
     In a non-limiting example, the microprocessor board  108  may be one of a Raspberry Pi series or other similar single-board computers. The microprocessor board  108  may include the processor, the GPIO pins to provide a physical interface between the microprocessor board  108  and external components, memory module such as a random access memory (RAM), one or more HDMI ports, camera serial interface (CSI) to connect the digital camera  106  through the FFC  112 , one or more USB ports to connect peripheral devices and power supply, one or more micro USB ports, and one or more wireless connectivity modules, such as Bluetooth and Wi-Fi and secure digital (SD) card slot. 
     In an aspect of the present disclosure, the Braille reading device  100  may be communication-enabled using various wired and wireless connectivity protocols, such as Wi-Fi and Personal Area Network. The processor of the Braille reading device  100  may load the trained deep learning based CNN model from a host machine or a server. 
       FIG. 2  depicts a schematic diagram of a 3D ring case  202 , according to exemplary aspects of the present disclosure. 
     According to aspects of the present disclosure the 3D ring case  202  is designed to be worn on a finger of the user and to accommodate the digital camera  106  and the switch  110 . The 3D ring case  202  corresponds to the 3D ring case  102  of  FIG. 1 . The 3D ring case  202  includes a first opening  203 , a second opening  204  below the first opening  203 , and a switch  206 . The digital camera  106  may be arranged in the first opening  203 . The 3D ring case  202  may be worn on a finger of the user by inserting the finger of the user in the second opening  204 . The switch  206  may be arranged on either of the lateral sides of the 3D ring case  202 . 
     Although the first opening  203  and the second opening  204  as shown in  FIG. 2  are round in shape, one or both the first opening  203  and the second opening  204  may be of any other shape depending on design requirements. For example, shape and size of the first opening  203  may be designed based on shape and size of the digital camera  106  to be arranged in the first opening  203 . 
     In an aspect of the present disclosure, the 3D ring case  202  may be custom designed to provide a slot, in place of the switch  206 , for accommodating the switch  110  on the 3D ring case  202 . The 3D ring case  202  may be custom designed to have dimensions in accordance with requirements and components of Braille reading device  100 . In an aspect of the present disclosure, the 3D ring case  202  may be printed by a 3D printer. 
     In an aspect of the present disclosure, the 3D ring case  202  may be designed in a plurality of sizes for the second opening  204 . In a non-limiting example, the 3D ring case  202  may be designed for three different sizes, i.e., small, medium, and large size of the second opening  204 , which can be selected to fit a finger size of a user. In an aspect of the present disclosure, the 3D ring case  202  may be designed such that size of the second opening  204  may be adjusted to fit size of the finger of the user. In an aspect of the present disclosure, the 3D ring case  202  may be custom designed in accordance with physical appearance preferences of different users. 
       FIG. 3A  depicts a schematic diagram of a microprocessor housing  304 , where (a) is a base and (b) is a lid of the microprocessor housing, according to exemplary aspects of the present disclosure. 
     According to aspects of the present disclosure the microprocessor housing  304  is designed to accommodate the microprocessor board  108 , the speaker  114 , and the battery. The microprocessor housing  304  corresponds to the microprocessor housing  104  of  FIG. 1 . The microprocessor housing  300  includes a base  303  and a lid  305 . The base  303  is designed to hold the microprocessor board  108  and the battery. The base  303  includes a strap  315 . The strap  315  is used to wear the microprocessor housing  304  on a wrist of the user as shown in  FIG. 3B . 
     The lid  305  includes a plurality of cutout portions  308  on sidewalls  306  (or lateral sides). The plurality of cut-out portions  308  may enable air flow in the microprocessor housing  304 . At least one of the plurality of cut-out portions  308  is used to arrange the speaker  114 . A top side of the lid  305  includes a cut-out portion  310 . The FFC  112  passes through the cut-out portion  310  to connect the digital camera  106  to the microprocessor board  108 . 
     The base  303  further includes a plurality of slots  312  and the sidewalls  306  include a plurality of tabs  314 . Each tab  314  aligns with a corresponding slot  312  when the lid  305  and the base  303  are joined together. 
     Although the plurality of cut-out portions  308  and the cut-out portion  310  as shown in  FIG. 3A  are round in shape, the plurality of cut-out portions  308  and the cut-out portion  310  may be of any other shape depending on design requirements. 
       FIG. 3B  depicts a schematic diagram of a microprocessor housing  304 , according to exemplary aspects of the present disclosure. The base  303  includes the strap  315 . In an aspect of the present disclosure, the strap  315  may be an elastic band which may be worn on the wrist of the user by stretching the elastic band and slipping (or sliding) the hand through the elastic band. In another aspect of the present disclosure, the strap  315  may include a first elastic wristband connector and a second elastic wristband connector. The first elastic wristband connector and the second elastic wristband connector may be coupled together to wear the microprocessor housing  304  on a wrist of the user. In an aspect of the present disclosure, the base  303  may include coupling means, such as fasteners, hooks, clips, and buckles to couple the strap  315  with the base  303 . The coupling means may be arranged on exterior sides or exterior bottom of the base  303 . In an aspect of the present disclosure, the base  303  may include slots on opposite side and the strap  315  may pass through the slots. 
     In an aspect of the present disclosure, the microprocessor housing  304  may be custom designed to have dimensions in accordance with requirements and components of Braille reading device  100 . In an aspect of the present disclosure, the microprocessor housing  304  may be printed by a 3D printer. 
     In an aspect of the present disclosure, the microprocessor housing  304  may be designed to have a slim profile such that the microprocessor housing  304  can be hidden under sleeve of garment worn by the user. In an aspect of the present disclosure, the microprocessor housing  304  may be designed in the shape of one or more cartoon characters, superhero characters, animals, or may be custom designed to any desired shape and size, so as to appeal to children learning Braille. 
       FIG. 4  depicts a process flow diagram  400  illustrating conversion of Braille dots to speech, according to exemplary aspects of the present disclosure. As shown in  FIG. 4 , a user may wear the Braille reading device  100 . The user may wear the microprocessor housing  104  on a wrist and the 3D ring case  102  on a finger. The user may scan a Braille script  402  with the finger to sense Braille dots. The Braille script  402  may be presented on one or more of: a Braille paper or textbook with printed dots, a refreshable Braille display device with dots or round-tipped pins raised through holes in a flat surface, a Braille input device, and public notices, bathroom doors, and signboards and the like. 
     In an aspect of the present disclosure, the user may wear the 3D ring case  102  on the index finger of a hand. For ease of operation, the user may press the switch  110  with the thumb of the same hand. In an aspect of the present disclosure, the user may wear the 3D ring case  102  in any finger on either hand, and use any finger on either hand to press the switch  110 . 
     In an aspect of the present invention, the user may wear the 3D ring case  102  on the same finger with which the user senses the Braille dots. One or more of focal length, position, and optical angle of the digital camera  106  may be appropriately adjusted to capture images of the Braille dots sensed by the user. 
     By sensing the Braille dots with the finger, the user may identify the location of the Braille dots. After the user identifies the location of the Braille dots, the user actuates the digital camera  106 . In an aspect of the present disclosure, after sensing the Braille dots, the user may move the finger behind the Braille dots such that the finger does not overlap the Braille dots and the Braille dots are in the field of view of the digital camera  106 . In an aspect of the present disclosure, optical axis and/or field of view of the digital camera  106  may be oriented to capture Braille images of the Braille dots in one or more directions with respect to the finger of the user. The digital camera  106  may capture Braille images of the Braille dots above the finger and on the right and left side of the finger. In an aspect of the present disclosure, the microprocessor may be configured to adjust the optical axis and/or field of view of the digital camera  106  based on user preferences. In an aspect of the present disclosure, the user may manually adjust the optical axis and/or field of view of the digital camera  106  by adjusting position and/or orientation of the digital camera  106  in the first opening  203 . 
     The user presses the switch  110  to actuate the digital camera  106  to capture a Braille image of the Braille dots. The digital camera  106  transmits the Braille image to the microprocessor included in the microprocessor board  108  over the FFC  112 . 
     In an aspect of the present disclosure, when the user presses the switch  110 , the digital camera  106  may begin to capture the Braille images at predetermined time intervals. The user may press the switch  110  again to stop capturing the Braille images. The microprocessor may be configured to capture the Braille images at the predetermined time intervals. The predetermined time interval may be adjusted based on reading speed and/or proficiency of the user. 
     The microprocessor may apply one or more digital image processing and/or computer vision techniques or algorithms to process the Braille image. The Braille script  402  is comprised of a plurality of Braille cells, each representing a character or a word. A Braille cell is represented by a collection of 6 raised dots arranged in two columns, each having three dots. The Braille image captured by the digital camera  106  may include one or more Braille cells, each representing a character or a word. 
     The microprocessor processes the Braille image by applying an image segmentation process  404  to the Braille image. Applying the image segmentation process  404  to the Braille image segments the Braille image into a plurality of segments. The plurality of segments in the segmented Braille image distinguishes individual Braille cells in the Braille image. The image segmentation process  404  may also distinguish the Braille cells from the background in the captured Braille image. In an aspect of the present disclosure, one or more image segmentation algorithms may be used to distinguish the Braille cells from the background and to distinguish individual Braille cells across a single line and/or multiple lines in the Braille image. 
     The microprocessor may further process the Braille image by applying an image noise reduction process  406  to the segmented Braille image. The quality of the Braille image captured by the digital camera  106  may be degraded due to various factors, such as a non-uniform ambient illumination, a low resolution imaging sensor, and an impulse noise. The image noise reduction process  406  may apply one or more image enhancement and denoising techniques to remove noise and enhance the quality of the segmented Braille image. The output of the image noise reduction process  406  is a denoised segmented Braille image. 
     The microprocessor may further process the Braille image by applying an image resizing process  408  to the denoised segmented Braille image. The plurality of segments in the denoised segmented Braille image may be appropriately resized for recognition of character or word corresponding to each of the plurality of Braille cells. One or more image interpolation and resizing techniques may be used to resize the plurality of denoised segments in the Braille image to generate a resized Braille image. 
     The microprocessor performs an image recognition process  410  on the resized Braille image. To perform the image recognition process  410 , the resized Braille image is input to the trained deep learning based CNN model. The image recognition process  410  is performed to recognize characters or words corresponding to each of the plurality of Braille cells, i.e., the plurality of segments. 
     In an aspect of the present disclosure, the deep learning based CNN model may be trained on a dataset of Braille scripts. The dataset of Braille scripts which train the deep learning based CNN model may include a plurality of labeled images of Braille scripts in one or more languages. The plurality of labeled images of Braille scripts may include alphabets, numerals, and text. For example, dataset of Braille scripts may include one or more of Arabic Braille numerals, Arabic Braille alphabet, Arabic Braille text, Arabic numerals, Arabic alphabet, Arabic text, English Braille numerals, English Braille alphabet, English Braille text, English numerals, English alphabet and English text. The plurality of labeled images of Braille scripts generates a plurality of Braille script recognition classes. The dataset of Braille scripts is not limited to English or Arabic and may also include numerals, alphabet and text in any language in which Braille is used. 
     The resized Braille image is input to the trained deep learning based CNN model to recognize characters or words corresponding to the Braille image. The trained deep learning based CNN model is a sequence of convolutional layer and pooling layer. The important features of the Braille image are kept in the convolution layers and intensified in the pooling layers and kept over the network, while discarding all the unnecessary information. The convolutional layers and pooling layers are consecutively connected to extract features of the resized Braille image to generate a features map. A flattening function may then be applied to the features map. The flattened features map may be passed through a neural network of fully connected layers or dense layers followed by an output layer. In the output layer, the Braille script recognition class is determined for the resized Braille image input to the trained deep learning based CNN model. The image recognition is complete, and the text corresponding to the captured Braille image is determined. 
     The processor may generate or extract an audio waveform of the text corresponding to the captured Braille image and transmit the audio waveform to the speaker  114 . The speaker  114  may generate a sound corresponding to the audio waveform of the text corresponding to the captured Braille image. In an aspect of the present disclosure, the microprocessor may be configured to change the sound corresponding to the audio waveform. For example, the user may select the sound to be a man&#39;s voice, a woman&#39;s voice, a child&#39;s voice, a cartoon character&#39;s voice, and any other voice from a selection of voice options. 
     In an aspect of the present disclosure, the microprocessor may connect to a computing device, such as a mobile phone or a server computing device to configure the sound of the Braille reading device  100 . In an aspect of the present disclosure, the Braille reading device  100  may pair with a mobile computing device running an application corresponding to the Braille reading device  100 . The user may adjust one or more configurations of the Braille reading device  100  via the application running on the mobile computing device. In an aspect of the present disclosure, the application may provide a user interface to present the learning progress of the user. 
     In an aspect display of the present disclosure, a display device may be connected to the digital camera  106 . At least one of a written text or a picture representative of the text corresponding to the captured Braille image may be displayed on the display device. 
     In an aspect of the present disclosure, the trained deep learning based CNN model may be implemented using open source Python libraries, such as TensorFlow, Keras, NumPy, and OpenCV. 
     In an aspect of the present disclosure, the microprocessor may be configured to run a programming code written in a programming language, such as R and Python. Running the programming code may cause the microprocessor to run the deep learning based CNN model to start the recognition process of the captured Braille image, and play an audio representation of the recognized text corresponding to the captured Braille image. In an aspect of the present disclosure, the trained deep learning based CNN model may be programmed on a host machine and configured on the microprocessor. The microprocessor may connect with the host machine to receive any updates related to the trained deep learning based CNN model and/or digital image processing. 
     Aspects of the present disclosure may enable a user, such as a visually impaired person or any other person reading or learning the Braille script to understand Braille scripts without assistance of a trainer. The user may sense the Braille dots and activate the digital camera  106  to capture the Braille image. The processor may process the captured Braille image and access the trained deep learning based CNN model to recognize text corresponding to the Braille image. Sounds of the recognized text may be played by the speaker  114  and/or displayed on a display screen. Thus, the Braille reading device  100  of the present disclosure provides real-time learning experience for the user with minimal or no assistance. 
     In an aspect of the present disclosure, the deep learning based CNN model may be trained on a dataset of Arabic braille numerals, alphabet, and text. Approximately 50,500 images may be collected for the dataset consisting of 10 classes for numerals, 28 classes for alphabet, and 60 classes of words in the Arabic language. Similar concept for training the deep learning based CNN model may be applied for other languages, thus, making the Braille reading device  100  compatible with any other language. The microprocessor may load the trained deep learning based CNN model for any other language to recognize text corresponding to Braille in that language. 
       FIG. 5  shows an exemplary process flow  500  of the present invention illustrating a method for converting printed Braille dots to speech, according to exemplary aspects of the present disclosure. 
     At step  502 , the method includes capturing a Braille image of the printed Braille dots. 
     At step  504 , the method includes matching the Braille image to a textural character corresponding to the Braille image. Data processing and one or more image recognition operations may be performed to match the Braille image to the textural character. 
     At step  506 , the method includes converting the textural character to an audio waveform. 
     At step  508 , the method includes transmitting the audio waveform to the speaker  114 . 
     At step  510 , the method includes generating a sound representative of a spoken word corresponding to the textural character. The speaker  114  generates the sound representative of the spoken word. 
       FIG. 6  shows an exemplary process flow  600  of the present invention illustrating a method for converting printed Braille dots to speech, according to exemplary aspects of the present disclosure. 
     At step  602 , the method includes directing a lens of the digital camera  106  towards Braille text including printed Braille dots. The digital camera is confined in the 3D ring case  102 . 
     At step  604 , the method includes compressing the switch  110  to capture a Braille image of the printed Braille dots. 
     At step  606 , the method includes matching the Braille image to a textural character corresponding to the Braille image. The microprocessor is configured to perform data processing and one or more image recognition operations to match the Braille image to the textural character. 
     At step  608 , the method includes converting the textural character to an audio waveform. 
     At step  610 , the method includes transmitting the audio waveform to the speaker  114 . 
     At step  612 , the method includes generating a sound representative of a spoken word corresponding to the textural character. The speaker  114  generates the sound representative of the spoken word. 
     The first embodiment is illustrated with respect to  FIGS. 1-9 . The first embodiment describes a system for converting printed Braille dots to speech. The system comprises a 3D ring case  102 ,  200 , a digital camera  106  mounted in the 3D ring case  102 ,  200 , wherein the digital camera  106  is configured to capture a Braille image of the printed Braille dots, a rechargeable battery, a speaker  114 , a microprocessor operatively connected to the rechargeable battery, the digital camera  106  and the speaker  114 , the microprocessor configured to perform data processing and one or more image recognition operations which match the Braille image to a textural character corresponding to the Braille image, convert the textural character to an audio waveform, and transmit the audio waveform to the speaker  114 , and wherein the speaker  114  is configured to receive the audio waveform and generate a sound representative of a spoken word corresponding to the textural character. 
     The printed Braille dots are configured to represent an Arabic Braille textural character and the speaker  114  is configured to output a sound representative of an Arabic spoken word. 
     The textural character is configured to include one or more letters, one or more numbers, or one or more words. 
     The one or more letters include an Arabic letter or an English letter, the one or more numbers include an Arabic number or an English number, and the one or more words include an Arabic word or an English word. 
     The system further comprises a display connected to the digital camera  106 , wherein the display is configured to display one of a written word or a picture representative of the word corresponding to the Braille image. 
     The rechargeable battery is a lithium battery. 
     The digital camera  106  is connected to the microprocessor by a serial bus configured to transmit data signals representative of the Braille image from the digital camera  106  to the microprocessor. 
     The 3D ring case  102 ,  200  includes a first ring configured to receive the digital camera  106 , a second ring configured to be worn on a finger of a user, wherein the second ring is arranged below the first ring, and a switch configured to actuate the digital camera  106  to capture the Braille image. 
     The system further comprises a microprocessor housing. The microprocessor housing includes a base configured to hold the microprocessor and the rechargeable battery, the base having a first elastic wristband connector and a second elastic wristband connector, a lid configured with sidewalls having a first plurality of cut outs configured for air flow and a second cut out configured to hold the speaker  114 , wherein a top of the lid has a third cut out configured to receive the serial bus, and wherein the base includes a plurality of slots around its periphery and the sidewalls include a plurality of tabs, wherein each tab is configured to align with a respective slot when the lid and the base are joined together. 
     The system further comprises a serial bus port located on the microprocessor; a power port configured to connect the rechargeable battery to the microprocessor, a first output pin configured to connect to a first wire, wherein the first wire is connected to a power input of the push button switch, a second output pin configured to connect a second wire, wherein the second wire is connected to a power output of the digital camera  106 , and a third output pin configured to connect to the speaker  114 . 
     The microprocessor is configured to perform the data processing by segmenting the Braille image into a plurality of segments, reducing image noise from the plurality of segments to generate a plurality of denoised segments, and resizing the plurality of denoised segments to generate a plurality of resized denoised segments. 
     The microprocessor is configured to perform one or more image recognition operations on the plurality of resized denoised segments to match the Braille image to a textural character corresponding to the Braille image by: training a deep learning based convolutional neural network on a dataset of Braille scripts, the Braille scripts including one or more of Arabic Braille numerals, Arabic Braille alphabet, Arabic Braille text, Arabic numerals, Arabic alphabet, Arabic text, English Braille numerals, English Braille alphabet, English Braille text, English numerals, English alphabet and English text to generate a plurality of Braille script recognition classes; applying the plurality of resized denoised segments to the deep learning based convolutional neural network, matching each of the plurality of resized denoised segments to a Braille script recognition class, and retrieving the audio waveform associated with the Braille script recognition class. 
     The second embodiment is illustrated with respect to  FIGS. 1-9 . The second embodiment describes a method for converting printed Braille dots to speech. The method comprising capturing a Braille image of the printed Braille dots, matching, by performing data processing and one or more image recognition operations, the Braille image to a textural character corresponding to the Braille image, converting the textural character to an audio waveform, transmitting the audio waveform to a speaker  114 , and generating, by the speaker  114 , a sound representative of a spoken word corresponding to the textural character. 
     The method further comprising segmenting the Braille image into a plurality of segments, reducing image noise from the plurality of segments to generate a plurality of denoised segments, and resizing the plurality of denoised segments to generate a plurality of resized denoised segments. 
     The method further comprising training a deep learning based convolutional neural network on a dataset of Braille scripts, the Braille scripts including one or more of Arabic Braille numerals, Arabic Braille alphabet, Arabic Braille text, Arabic numerals, Arabic alphabet, Arabic text, English Braille numerals, English Braille alphabet, English Braille text, English numerals, English alphabet and English text to generate a plurality of Braille script recognition classes; applying the plurality of resized denoised segments to the deep learning based convolutional neural network, and matching each of the plurality of resized denoised segments to a Braille script recognition class, and retrieving the audio waveform associated with the Braille script recognition class. 
     The third embodiment is illustrated with respect to  FIGS. 1-9 . The third embodiment describes a method for converting printed Braille dots to speech. The method comprising directing a lens of a digital camera  106  confined in a 3D ring case  102 ,  200  towards Braille text including printed Braille dots, compressing a push button to capture a Braille image of the printed Braille dots, matching, by a microprocessor configured to perform data processing and one or more image recognition operations, the Braille image to a textural character corresponding to the Braille image, converting the textural character to an audio waveform, transmitting the audio waveform to a speaker  114 , and generating, by the speaker  114 , a sound representative of a spoken word corresponding to the textural character. 
     The method further comprising segmenting the Braille image into a plurality of segments, reducing image noise from the plurality of segments to generate a plurality of denoised segments, and resizing the plurality of denoised segments to generate a plurality of resized denoised segments. 
     The method further comprising training a deep learning based convolutional neural network on a dataset of Braille scripts, the Braille scripts including one or more of Arabic Braille numerals, Arabic Braille alphabet, Arabic Braille text, Arabic numerals, Arabic alphabet, Arabic text, English Braille numerals, English Braille alphabet, English Braille text, English numerals, English alphabet and English text to generate a plurality of Braille script recognition classes, applying the plurality of resized denoised segments to the deep learning based convolutional neural network, matching each of the plurality of resized denoised segments to a Braille script recognition class, and retrieving the audio waveform associated with the Braille script recognition class. 
     The method further comprising transmitting, to the speaker, a sound representative of an Arabic spoken word corresponding to an Arabic textural character associated with Arabic Braille printed dots. 
     The method further comprising displaying, on the digital camera  106 , one of a written word or a picture representative of the textural character corresponding to the Braille image. 
       FIG. 7  is an illustration of a non-limiting example of details of computing hardware used in the computing system, according to exemplary aspects of the present disclosure. In  FIG. 7 , a controller  700  is described is representative of the system  600  of  FIG. 6  in which the controller is a computing device which includes a CPU  701  which performs the processes described above/below. The process data and instructions may be stored in memory  702 . These processes and instructions may also be stored on a storage medium disk  704  such as a hard drive (HDD) or portable storage medium or may be stored remotely. 
     Further, the claims are not limited by the form of the computer-readable media on which the instructions of the inventive process are stored. For example, the instructions may be stored on CDs, DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk or any other information processing device with which the computing device communicates, such as a server or computer. 
     Further, the claims may be provided as a utility application, background daemon, or component of an operating system, or combination thereof, executing in conjunction with CPU  701 ,  703  and an operating system such as Microsoft Windows 7, Microsoft Windows 10, UNIX, Solaris, LINUX, Apple MAC-OS, and other systems known to those skilled in the art. 
     The hardware elements in order to achieve the computing device may be realized by various circuitry elements, known to those skilled in the art. For example, CPU  701  or CPU  703  may be a Xenon or Core processor from Intel of America or an Opteron processor from AMD of America, or may be other processor types that would be recognized by one of ordinary skill in the art. Alternatively, the CPU  701 ,  703  may be implemented on an FPGA, ASIC, PLD or using discrete logic circuits, as one of ordinary skill in the art would recognize. Further, CPU  701 ,  703  may be implemented as multiple processors cooperatively working in parallel to perform the instructions of the inventive processes described above. 
     The computing device in  FIG. 7  also includes a network controller  706 , such as an Intel Ethernet PRO network interface card from Intel Corporation of America, for interfacing with network  760 . As can be appreciated, the network  760  can be a public network, such as the Internet, or a private network such as an LAN or WAN network, or any combination thereof and can also include PSTN or ISDN sub-networks. The network  760  can also be wired, such as an Ethernet network, or can be wireless such as a cellular network including EDGE, 3G and 4G wireless cellular systems. The wireless network can also be WiFi, Bluetooth, or any other wireless form of communication that is known. 
     The computing device further includes a display controller  708 , such as a NVIDIA GeForce GTX or Quadro graphics adaptor from NVIDIA Corporation of America for interfacing with display  710 , such as a Hewlett Packard HPL2445w LCD monitor. A general purpose I/O interface  712  interfaces with a keyboard and/or mouse  714  as well as a touch screen panel  716  on or separate from display  710 . General purpose I/O interface also connects to a variety of peripherals  718  including printers and scanners, such as an OfficeJet or DeskJet from Hewlett Packard. 
     A sound controller  720  is also provided in the computing device such as Sound Blaster X-Fi Titanium from Creative, to interface with speakers/microphone  722  thereby providing sounds and/or music. 
     The general purpose storage controller  724  connects the storage medium disk  704  with communication bus  726 , which may be an ISA, EISA, VESA, PCI, or similar, for interconnecting all of the components of the computing device. A description of the general features and functionality of the display  710 , keyboard and/or mouse  714 , as well as the display controller  708 , storage controller  724 , network controller  706 , sound controller  720 , and general purpose I/O interface  712  is omitted herein for brevity as these features are known. 
     The exemplary circuit elements described in the context of the present disclosure may be replaced with other elements and structured differently than the examples provided herein. Moreover, circuitry configured to perform features described herein may be implemented in multiple circuit units (e.g., chips), or the features may be combined in circuitry on a single chipset, as shown on  FIG. 8 . 
       FIG. 8  shows a schematic diagram of a data processing system, according to certain embodiments, for performing the functions of the exemplary embodiments. The data processing system is an example of a computer in which code or instructions implementing the processes of the illustrative embodiments may be located. 
     In  FIG. 8 , data processing system  800  employs a hub architecture including a north bridge and memory controller hub (NB/MCH)  825  and a south bridge and input/output (I/O) controller hub (SB/ICH)  820 . The central processing unit (CPU)  830  is connected to NB/MCH  825 . The NB/MCH  825  also connects to the memory  845  via a memory bus, and connects to the graphics processor  850  via an accelerated graphics port (AGP). The NB/MCH  825  also connects to the SB/ICH  820  via an internal bus (e.g., a unified media interface or a direct media interface). The CPU Processing unit  830  may contain one or more processors and even may be implemented using one or more heterogeneous processor systems. 
     For example,  FIG. 9  shows one implementation of CPU  830 . In one implementation, the instruction register  938  retrieves instructions from the fast memory  940 . At least part of these instructions are fetched from the instruction register  938  by the control logic  936  and interpreted according to the instruction set architecture of the CPU  830 . Part of the instructions can also be directed to the register  932 . In one implementation the instructions are decoded according to a hardwired method, and in another implementation the instructions are decoded according to a microprogram that translates instructions into sets of CPU configuration signals that are applied sequentially over multiple clock pulses. After fetching and decoding the instructions, the instructions are executed using the arithmetic logic unit (ALU)  934  that loads values from the register  932  and performs logical and mathematical operations on the loaded values according to the instructions. The results from these operations can be feedback into the register and/or stored in the fast memory  940 . According to certain implementations, the instruction set architecture of the CPU  830  can use a reduced instruction set architecture, a complex instruction set architecture, a vector processor architecture, a very large instruction word architecture. Furthermore, the CPU  830  can be based on the Von Neuman model or the Harvard model. The CPU  830  can be a digital signal processor, an FPGA, an ASIC, a PLA, a PLD, or a CPLD. Further, the CPU  830  can be an x86 processor by Intel or by AMD; an ARM processor, a Power architecture processor by, e.g., IBM; a SPARC architecture processor by Sun Microsystems or by Oracle; or other known CPU architecture. 
     Referring again to  FIG. 8 , the data processing system  800  can include that the SB/ICH  820  is coupled through a system bus to an I/O Bus, a read only memory (ROM)  856 , universal serial bus (USB) port  864 , a flash binary input/output system (BIOS)  868 , and a graphics controller  858 . PCI/PCIe devices can also be coupled to SB/ICH  888  through a PCI bus  862 . 
     The PCI devices may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. The Hard disk drive  860  and CD-ROM  866  can use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. In one implementation the I/O bus can include a super I/O (SIO) device. 
     Further, the hard disk drive (HDD)  860  and optical drive  866  can also be coupled to the SB/ICH  820  through a system bus. In one implementation, a keyboard  870 , a mouse  872 , a parallel port  878 , and a serial port  876  can be connected to the system bus through the I/O bus. Other peripherals and devices that can be connected to the SB/ICH  820  using a mass storage controller such as SATA or PATA, an Ethernet port, an ISA bus, a LPC bridge, SMBus, a DMA controller, and an Audio Codec. 
     Moreover, the present disclosure is not limited to the specific circuit elements described herein, nor is the present disclosure limited to the specific sizing and classification of these elements. For example, the skilled artisan will appreciate that the circuitry described herein may be adapted based on changes on battery sizing and chemistry, or based on the requirements of the intended back-up load to be powered. 
     Additionally, some implementations may be performed on modules or hardware not identical to those described. Accordingly, other implementations are within the scope that may be claimed. 
     The above-described hardware description is a non-limiting example of corresponding structure for performing the functionality described herein. 
     Obviously, numerous modifications and variations of the present disclosure are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described herein.