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A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion

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@article{IJASEIT9956,
   author = {Robert G. de Luna},
   title = {A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {1},
   year = {2020},
   pages = {128--136},
   keywords = {optical character recognition; braille; image processing; tesseract; text-to-braille.},
   abstract = {

This study provided a platform that converts printed text documents into corresponding braille code that will trigger the palpable output of the braille cells. The system is composed of two main parts: the image scanner and the microcontroller-based braille platform. The image scanner captures the printed text document and performs a series of pre-processing algorithms where the processed image will be subjected to character recognition using Tesseract. It is open-source software for character recognition capable of recognizing text characters in different fonts and sizes. SimpleCV, also an open-source software for computer vision and a simpler version of an OpenCV, was utilized in pre-processing of images where binarization, filtering, edge detection, and character segmentation are performed. This will allow the microcontroller-based braille platform to interpret the printed characters from the generated braille code in ASCII format that will trigger the palpable output of the braille cells. The developed system was subjected to functionality and accuracy testing to assess its performance. Accuracy was based on the capability of the system to produce the right braille outputs that match the scanned line of text which are in Arial font. The testing was conducted utilizing the Arial font size of 12, 14, 16, 18, 20, 22, and 24. Results show that the system is capable of recognizing text with greater than 85 % accuracy starting at font size 18 with an average accuracy of 88.09 % and increases accordingly as the font size increases.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9956},    doi = {10.18517/ijaseit.10.1.9956} }

EndNote

%A de Luna, Robert G.
%D 2020
%T A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion
%B 2020
%9 optical character recognition; braille; image processing; tesseract; text-to-braille.
%! A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion
%K optical character recognition; braille; image processing; tesseract; text-to-braille.
%X 

This study provided a platform that converts printed text documents into corresponding braille code that will trigger the palpable output of the braille cells. The system is composed of two main parts: the image scanner and the microcontroller-based braille platform. The image scanner captures the printed text document and performs a series of pre-processing algorithms where the processed image will be subjected to character recognition using Tesseract. It is open-source software for character recognition capable of recognizing text characters in different fonts and sizes. SimpleCV, also an open-source software for computer vision and a simpler version of an OpenCV, was utilized in pre-processing of images where binarization, filtering, edge detection, and character segmentation are performed. This will allow the microcontroller-based braille platform to interpret the printed characters from the generated braille code in ASCII format that will trigger the palpable output of the braille cells. The developed system was subjected to functionality and accuracy testing to assess its performance. Accuracy was based on the capability of the system to produce the right braille outputs that match the scanned line of text which are in Arial font. The testing was conducted utilizing the Arial font size of 12, 14, 16, 18, 20, 22, and 24. Results show that the system is capable of recognizing text with greater than 85 % accuracy starting at font size 18 with an average accuracy of 88.09 % and increases accordingly as the font size increases.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9956 %R doi:10.18517/ijaseit.10.1.9956 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 1 %@ 2088-5334

IEEE

Robert G. de Luna,"A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 1, pp. 128-136, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.1.9956.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - de Luna, Robert G.
PY  - 2020
TI  - A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 1
Y2  - 2020
SP  - 128
EP  - 136
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - optical character recognition; braille; image processing; tesseract; text-to-braille.
N2  - 

This study provided a platform that converts printed text documents into corresponding braille code that will trigger the palpable output of the braille cells. The system is composed of two main parts: the image scanner and the microcontroller-based braille platform. The image scanner captures the printed text document and performs a series of pre-processing algorithms where the processed image will be subjected to character recognition using Tesseract. It is open-source software for character recognition capable of recognizing text characters in different fonts and sizes. SimpleCV, also an open-source software for computer vision and a simpler version of an OpenCV, was utilized in pre-processing of images where binarization, filtering, edge detection, and character segmentation are performed. This will allow the microcontroller-based braille platform to interpret the printed characters from the generated braille code in ASCII format that will trigger the palpable output of the braille cells. The developed system was subjected to functionality and accuracy testing to assess its performance. Accuracy was based on the capability of the system to produce the right braille outputs that match the scanned line of text which are in Arial font. The testing was conducted utilizing the Arial font size of 12, 14, 16, 18, 20, 22, and 24. Results show that the system is capable of recognizing text with greater than 85 % accuracy starting at font size 18 with an average accuracy of 88.09 % and increases accordingly as the font size increases.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9956 DO - 10.18517/ijaseit.10.1.9956

RefWorks

RT Journal Article
ID 9956
A1 de Luna, Robert G.
T1 A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 1
YR 2020
SP 128
OP 136
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 optical character recognition; braille; image processing; tesseract; text-to-braille.
AB 

This study provided a platform that converts printed text documents into corresponding braille code that will trigger the palpable output of the braille cells. The system is composed of two main parts: the image scanner and the microcontroller-based braille platform. The image scanner captures the printed text document and performs a series of pre-processing algorithms where the processed image will be subjected to character recognition using Tesseract. It is open-source software for character recognition capable of recognizing text characters in different fonts and sizes. SimpleCV, also an open-source software for computer vision and a simpler version of an OpenCV, was utilized in pre-processing of images where binarization, filtering, edge detection, and character segmentation are performed. This will allow the microcontroller-based braille platform to interpret the printed characters from the generated braille code in ASCII format that will trigger the palpable output of the braille cells. The developed system was subjected to functionality and accuracy testing to assess its performance. Accuracy was based on the capability of the system to produce the right braille outputs that match the scanned line of text which are in Arial font. The testing was conducted utilizing the Arial font size of 12, 14, 16, 18, 20, 22, and 24. Results show that the system is capable of recognizing text with greater than 85 % accuracy starting at font size 18 with an average accuracy of 88.09 % and increases accordingly as the font size increases.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9956 DO - 10.18517/ijaseit.10.1.9956