Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Rifki Indra Perwira, Mangaras Yanu Florestiyanto, Indah Reforsiana Nurjanah, - Heriyanto, Dessyanto Boedi Prasetyo


Visually impaired people use braille letters to write and read, which not all people with normal vision can read. It causes parents of children with impaired vision to have difficulties in assisting them in learning at home. At the same time, the involvement of parents in children's learning assistance is needed to monitor their learning progress. In this research, braille letters are identified through images taken using a scanner as a tool to input the images. Then, the Canny edge detection method is used obtain all the edges of each braille dot. Feature extraction is applied to obtain all characteristics of each letter, and the method used is the Gabor Wavelet. The features which are utilized include standard deviation, mean, variance, and median with a theta angle of 00, 300, 450, 600, 1200, 1350, 1800 and wavelengths of 3, 6, 13, 28, and 58. These features are combined and used as test data and training data for the Support Vector Machine (SVM) classification stage and produce letters and words in alphabetic letter forms. Braille letters that can be identified in this research are small letters, capital letters, punctuation marks, and numbers. Tests are carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall. Based on the results of the tests conducted using 758 braille letters, the accuracy value is 98.15%; the precision value is 97.66%; and the recall value is 98.28%.


Braille; image processing; canny edge detection; gabor wavelet; support vector machine.

Full Text:



D. Berthelsen and S. Walker, "Parent's Involvement in Their Children's Education," Family Matters, no. 79, pp. 34-41, 2008.

M. Khochen-Bagshaw, "Reading through touch, importance and challenges," in World Congress Braille 21, Germany, 2011.

K. Smelyakov, A. Chupryna, A. Sakhon, D. Yeremenko and V. Polezhai, "Braille Character Recognition Based on Neural Networks," IEEE Second International Conference on Data Stream Mining & Processin, pp. 509-513, 2018.

A. Mousa, H. A. R. Hiary and L. Alnemer, "Smart Braille System Recognize," IJCSI International Journal of Computer Science Issues, vol. 10, no. 6, pp. 52-60, 2013.

A. Antonscopoulous and D. Bridson, "A Robust Braille Recognition System," Springer Lecture Notes in Computer Science, pp. 533-545, 2004.

J. Subur, T. A. Sardjono and R. Mardiyanto, "Braille Character Recognition Using Find Contour Method," in the 5th International Conference on Electrical Engineering and Informatic, Bali, Indonesia, 2015.

S. Ibrahim, N. A. Tarmizi, N. Sabri, N. F. M. Johari and A. F. A. Fadzil, "Braille Image Recognition for Beginners," in 2018 9th IEEE Control and System Graduate Research Colloquium (ICSGRC 2018), Shah Alam, Malaysia, 2018.

B. Nurgroho, I. Ardiyanto and H. A. Nugroho, "Review of Optical Braille Recognition Using Camera for Image Acquisition," in 2nd International Conference on Biomedical Engineering (IBIOMED), 2018.

V. V. Murthy and M. Hanumanthappa, "Improving Optical Braille Recognition in Pre-processing stage," in 2018 International Conference on Soft-computing and Network Security (ICSNS), 2018.

N. D. Silva and S. Vasanthapriyan, "Optical Sinhala Braille Documents Convertion Methodology for Sinhala Language," in 2018 National Information Technology Conference (NITC), Colombo, Sri Lanka, 2018.

R. Li, H. Liu, J. X. X. Wang and Y. Qian, "Optical Braille Recognition Based on Semantic Segmentation Network with Auxiliary Learning Strategy," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020.

K. Rączy, M. Czarnecka, D. Zaremba, K. Izdebska, M. PaplinÌska, G. Hesselmann, A. Knops and M. Szwed, "A shared code for Braille and Arabic digits revealed by cross-modal priming T in sighted Braille readers," Acta Psychologica, 2020.

A. Sharma, S. Devi and J. K. Verma, "Braille Book Reader using Raspberry Pi," in 2020 International Conference on Computational Performance Evaluation (ComPE), India, 2020.

M. R. Phangtriastu, J. Harefa and D. F. Tanoto, "Comparison Between Neural Network and Support Vector Machine In Optical Character Support Vector Machine In Optical Character Recognition," Procedia Computer Science, pp. 351-357, 2017.

A. A. Chandini and U. M. B., "Improved Quality Detection Technique for Fruits Using GLCM and MultiClass SVM," in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, 2018.

M. F. Zuandi, B. Hidayat and S. Sitam, "Granuloma Image Detection Through Periapical Radiograph by Using Gabor Wavelet Method and Support Vector Machine Classification," in 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, 2018.

I. Wicaksono, H. Kusuma and T. A. Sardjono, "Traffic Sign Image Recognition Using Gabor Wavelet and Principle Component Analysis," in 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), Yogyakarta, Indonesia, 2019.

D. Kathuria and J. Yadav, "An Improved Illumination Invariant Face Recognition Based on Gabor Wavelet Transform," in 2018 Conference on Information and Communication Technology (CICT), Jabalpur, India, 2018.

Y. Wang, H. Liu, D. Wang and D. Liu, "Image processing in fault identification for power equipment based on improved super green algorithm," Computers & Electrical Engineering, vol. 87, 2020.

A. Prasetio and P. M. Hasugian, "Improving the Quality of Digital Images Using the Median Filter Technique to Reduce Noise," Journal Publications & Informatics Engineering Research, vol. 4, no. 1, pp. 143-148, 2019.

V. Mohan, R. K. Durga, S. Devathi and K. S. Raju, "Image Processing Representation Using Binary Image; Grayscale, Color Image, and Histogram," in Proceedings of the Second International Conference on Computer and Communication Technologies, New Delhi, 2016.

Y. Zhu, L. Wang, Y. Behnamian, S. Song, R. Wang, Z. Gao, W. Hu and D.-H. Xia, "Metal pitting corrosion characterized by scanning acoustic microscopy and binary image processing," Corrosion Science, vol. 170, 2020.

K. Somasundaram, J. H. Mercina, S. Magesh and T. Kalaiselvi, "Brain Portion Extraction Scheme using Region Growing and Morphological Operation from MRI of Human Head Scans," International Journal of Computer Sciences and Engineering, vol. 6, no. 4, pp. 41-51, 2018.

Q. Wang, N. Gui, Y. Liu, S. Peng, X. Yang, J. Tu and S. Jiang, "A morphological image processing method for simultaneous scrutinization of particle position and velocity in pebble flow," Annals of Nuclear Energy, vol. 148, no. 107704, 2020.

I. S. Masad, A. Al-Fahoum and I. Abu-Qasmieh, "Automated measurements of lumbar lordosis in T2-MR images usingdecision tree classifier and morphological image processing," Engineering Science and Technology,an International Journal, vol. 22, pp. 1027-1034, 2019.

T. R. Fujimoto, T. Kawasaki and K. Kitamura, "Canny-Edge-Detection/Rankine-Hugoniot-conditions unified shock sensor for inviscid and viscous flows," Journal of Computational Physics, vol. 396, pp. 264-279, 2019.

U. A. Nnolim, "Automated crack segmentation via saturation channel thresholding, areaclassification and fusion of modified level set segmentation with Cannyedge detection," Heliyon, vol. 6, no. 12, 2020.

G. Ayalew, Q. U. Zaman, A. W. Schumann, D. C. Percival and Y. K. Chang, "An investigation into the potential of Gabor wavelet features for sceneclassification in wild blueberryfields," Artificial Intelligence in Agriculture, vol. 5, pp. 72-81, 2021.

D. A. H. Al-Fayadh, H. R. Mohammed and R. S. Al-shimsah, "Gabor Wavelet Transform in Image Compression," Journal of Kufa for Mathematics and Computer, vol. 1, pp. 107-113, 2012.

Adiwijaya, B. Purnama, A. Hasyim, M. D. Septiani, U. N. Wisesty and W. Astuti, "Follicle Detection on the USG Images to Support Determination of Polycystic Ovary Syndrome," Journal of Physics: Conference Series, vol. 622, 2015.

C. Adak, "Gabor Filter and Rough Clustering Based Edge Detection," in International Conference on Human Computer Interactions (ICHCI), Chennai, India, 2014.

E. Gul, N. Alpaslan and M. E. Emiroglu, "Robust optimization of SVM hyper-parameters for spillway type selection," Ain Shams Engineering Journal, 2021.

K. Thirumala, S. Pal , T. Jain and A. C. Umarikar , "A classification method for multiple power quality disturbances using EWT based adaptive filtering and multi-class SVM," Neurocomputing, vol. 334, pp. 265-274, 2019.

Z. Wang and X. Xue, "Multi-Class Support Vector Machine," Ma Y., Guo G. (eds) Support Vector Machines Applications, pp. 23-48, 2014.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development