Braille Letter Recognition System Using Website-Based Deep Learning Methods
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Published:
Jul 24, 2024
Keywords:
Computer Vision, Convolutional Neural Network, Deep Learning, Braille Letter
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Abstract
Braille letters are used as a written language for people with visual impairments. To this day, Braille letters are used in in inclusive schools where they are taught to disabled students. However, there are physical capability barriers faced by teachers when correcting Braille answer sheets written by visually impaired students. The ability to read Braille letters is also important for family members to support the students' learning process. This research’s purpose was to create a system that can transliterate Braille letters into the Latin alphabet using deep learning methods. The proposed deep learning methods include Base Convolutional Neural Network (CNN), ResNet50, VGG-16, and Inception-v3. The Braille Character image dataset used consists of 12,641 data divided into 37 classes from the AEyeAlliance repository. The Base CNN model used achieved 98% training accuracy, 99% validation accuracy, and 99.1% testing accuracy.
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How to Cite
I Made Agus Dwi Suarjaya, Wiratama, B. A., & Ayu Wirdiani. (2024). Braille Letter Recognition System Using Website-Based Deep Learning Methods. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 5(3), 93 - 99. https://doi.org/10.62527/jitsi.5.3.244
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References
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[3] Isayed, S., & Tahboub, R. (2015, August 19). A review of optical Braille recognition. 2015 2nd World Symposium on Web Applications and Networking, WSWAN 2015. https://doi.org/10.1109/WSWAN.2015.7210343Garnier, A., Fortin, D., Zoll, J., N’Guessan, B., Mettauer, B., Lampert, E., Veksler V., & Ventura-Clapier, R. (2005). Coordinated changes in mitochondrial function and biogenesis in healthy and diseased human skeletal muscle. The FASEB Journal, 19(1), 43-52
[4] Hasnat, A., & Nasipuri, M. (2013). Development of a Bangla Character Recognition (BCR) System for Generation of Bengali Text from Braille Notation. In International Journal of Innovative Technology and Exploring Engineering (IJITEE) (Issue 3).
[5] Padmavathi, S., K.S.S, M., Reddy .S, S., & D, Meenakshy. (2013). Conversion of Braille to Text in English, Hindi and Tamil Languages. International Journal of Computer Science, Engineering and Applications, 3(3), 19–32.
[6] T, Shreekanth., & Udayashankara, V. (2013). A Review on Software Algorithms for Optical Recognition of Embossed Braille Characters. International Journal of Computer Applications, 81(3), 25–35.
[7] Murray, I., & Pasquale, A. (2006). A portable device for the translation of braille to text. Eighth International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2006, 2006, 231–232.
[8] Venugopal-Wairagade, G. A. (2016). Braille Recognition using a Camera-enabled Smartphone. International Journal of Engineering and Manufacturing, 6(4), 32–39.
[9] Scholar, R. (2014). An Application of Eight Connectivity based Two-pass Connected-Component
Labelling Algorithm For Double Sided Braille Dot Recognition. In International Journal of Image
Processing (IJIP) (Issue 8).
[10] Antonacopoulos, A., & Bridson, D. (2004). A robust braille recognition system. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3163, 533–545.
[11] Khalil, S., Al-Shamma, S. D., & Fathi, S. (2010). Arabic Braille Recognition and Transcription into Text and Voice The Applications of Tele-diagnostic for Disease Detection and Management Project View project Arabic Braille Recognition and Transcription into Text and Voice.
[12] Ng, C. M., Ng, V., & Lau, Y. (1999). Regular feature extraction for recognition of Braille. Proceedings - 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 1999, 302–306.
[13] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection.
[14] Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition.
[15] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 770–778.
[16] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 2818–2826.
[17] Github.com. (2023, 28 Maret). DotNeuralNet. Diakses pada 15 Juni 2023, dari https://github.com/snoop2head/DotNeuralNet.
[18] Li, R., Liu, H., Wang, X., & Qian, Y. (2018). DSBI: Double-Sided Braille image dataset and algorithm evaluation for Braille dots detection. ACM International Conference Proceeding Series, January, 65–69.
[19] Ovodov, I. G. (2021). Optical Braille Recognition Using Object Detection Neural Network. Proceedings of the IEEE International Conference on Computer Vision, 2021-Octob, 1741–1748.
[20] Lu, L., Wu, D., Xiong, J., Liang, Z., & Huang, F. (2022). Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images. Computational Intelligence and Neuroscience, 2022.
[21] Github.com. (2018, 9 September). Aeye-alliance. Diakses pada 15 Juni 2023, dari https://github.com/HelenGezahegn/aeye-alliance.
[22]Hidra Amnur, Y. Syanurdi, R. Idmayanti, and A. Erianda, “Developing Online Learning Applications for People with Hearing Impairment,” vol. 5, no. March, pp. 32–38, 2021, [Online]. Available: www.joiv.org/index.php/joiv
[23] M, H., & M.N, S. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 01–11.
[24] Viering, T., & Loog, M. (2021). The Shape of Learning Curves : a Review. 1–20.
[25] Ghasemian, A., Hosseinmardi, H., & Clauset, A. (2020). Evaluating overfit and underfit in models of network community structure. IEEE Transactions on Knowledge and Data Engineering, 32(9), 1722–1735.
[26] Zhang, S., & Yoshino, K. (2007). A Braille Recognition System by the Mobile Phone with Embedded Camera. Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), 223-223.
[27] Morgavi, G., and Morando. M. "A neural network hybrid model for an optical braille recognitor." International Conference on Signal, Speech and Image Processing 2002 (ICOSSIP 2002). 2002.