Utilizing Convolutional Neural Network for Learning Web-Based Braille Letter Classification System

Ahmad Ridwan; Yoan  Purbolingga; Hanisah Hanisah

doi:10.47709/cnahpc.v6i1.3386

Authors

Ahmad Ridwan Department of Informatics, Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia
Yoan Purbolingga Department of Electrical Engineering, Institut Teknologi Bisnis Riau, Pekanbaru, Indonesia
Hanisah Master of Public Health, Universitas Sebelas Maret, Surakarta, Indonesia

DOI:

10.47709/cnahpc.v6i1.3386

Keywords:

Braille Letter, Convolutional Neural Network, Image Data, ReLU, Softmax

Dimension Badge Record

Abstract

This paper aims to facilitate prospective teachers and people who want to learn braille letters. The system designed is a website that will classify braille letters using the convolutional neural network (CNN) method with the activation functions used, namely ReLU and Softmax. In this research, the input is an image of braille letters with grayscale elements. The output of the data is a regular alphabet letter. Most of this research data consists of training and testing data, which is 2,722 pieces. The accuracy results obtained in the data training process using Max Pooling and epoch 30 for data is 92.15%, epoch 50 is 94.58%, and for training data with epoch 100 is 96.64%. The test results using the system produce an accuracy value of all braille letter image data of 92.30%. Furthermore, for better system development, it is recommended to use hyperparameter tuning to minimize classification uncertainty in braille letter images.

Downloads

Download data is not yet available.

Google Scholar Cite Analysis
Abstract viewed = 200 times

References

Bhatia, S., Devi, A., Alsuwailem, R. I., & Mashat, A. (2022). Convolutional Neural Network Based Real Time Arabic Speech Recognition to Arabic Braille for Hearing and Visually Impaired. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.898355

Gonçalves, D., Santos, G. G., Campos, M. B., Amory, A. M., & Manssour, I. H. (2020). Braille character detection using deep neural networks for an educational robot for visually impaired people. 2020: Anais Do XVI Workshop de Visão Computacional, 123–128. https://github.com/lsa-pucrs/donnie-assistive-robot-sw

Hassan, K. M. N., Biswas, S. K., Anwar, M. S., Siam, M. S. I., & Shahnaz, C. (2019). A Dual-Purpose Refreshable Braille Display Based on Real Time Object Detection and Optical Character Recognition. 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON). https://doi.org/10.1109/SPICSCON48833.2019.9065110

Haylekiros Assefa, T., & Hailekiros Assefa, T. (2018). Design of Neural Network System to Communicate a Blind Person with a Computer Using a Braille. Innovative Systems Design and Engineering, 9(1), 19–29. https://doi.org/10.7176/ISDE/9-1-04

Herlambang, M. F., Hermana, A. N., & Putra, K. R. (2021). Pengenalan Karakter Huruf Braille dengan Metode Convolutional Neural Network. Systemic: Information System and Informatics Journal, 6(2), 20–26. https://doi.org/10.29080/systemic.v6i2.969

Hossain, S., Raied, A. A., Rahman, A., Abdullah, Z. R., Adhikary, D., Khan, A. R., Bhattacharjee, A., Shahnaz, C., & Fattah, S. A. (2018). Text to Braille Scanner with Ultra Low-Cost Refreshable Braille Display. 2018 IEEE Global Humanitarian Technology Conference (GHTC). https://doi.org/10.1109/GHTC.2018.8601552

Kausar, T., Manzoor, S., Kausar, A., Lu, Y., Wasif, M., & Adnan Ashraf, M. (2021). Deep Learning Strategy for Braille Character Recognition. IEEE Access, 9, 169357–169371. https://doi.org/10.1109/ACCESS.2021.3138240

Lecun, Y., Bottou, E., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11). https://doi.org/10.1109/5.726791

Li, C., & Yan, W. (2021). Braille Recognition Using Deep Learning. Proceedings of the 4th International Conference on Control and Computer Vision, 30–35. https://doi.org/10.1145/3484274.3484280

Li, T., Zeng, X., & Xu, S. (2014). A Deep Learning Method for Braille Recognition. 2014 Sixth International Conference on Computational Intelligence and Communication Networks (CICN). https://doi.org/10.1109/CICN.2014.229

Made, I., Agastya, A., Oktarina, D., Handayani, D., & Mantoro, T. (2019). A Systematic Literature Review of Deep Learning Algorithms for Personality Trait Recognition. 2019 5th International Conference on Computing Engineering and Design (ICCED). https://doi.org/10.1109/ICCED46541.2019.9161107

Mccarthy, T., Rosenblum, L. P., Johnson, B. G., Dittel, J., & Kearns, D. M. (2016). An Artificial Intelligence Tutor: A Supplementary Tool for Teaching and Practicing Braille. Journal of Visual Impairment & Blindness, 110(5), 309–322. https://doi.org/10.1177/0145482X1611000503

Molina, S., Pérez, B., & Gómez, J. (2016). Literary Braille language translator to Spanish text. 2016 IEEE International Conference on Automatica (ICA-ACCA). https://doi.org/10.1109/ICA-ACCA.2016.7778514

Murthy, V. V., & Hanumanthappa, M. (2018). Improving Optical Braille Recognition in Pre-processing stage. 2018 International Conference on Soft-Computing and Network Security (ICSNS). https://doi.org/10.1109/ICCIMA

Ovodov, I. G. (2021). Semantic-based Annotation Enhancement Algorithm for Semi-supervised Machine Learning Efficiency Improvement Applied to Optical Braille Recognition. 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2190–2194. https://doi.org/10.1109/ElConRus51938.2021.9396534

Ramiati, Aulia, S., & Lifwarda, L. (2020). Aplikasi Identifikasi Huruf Braille Menggunakan Computer Vision Berbasis Raspberry Pi. JURNAL NASIONAL TEKNIK ELEKTRO, 9(1), 12. https://doi.org/10.25077/jnte.v9n1.707.2020

Ramiati, Aulia, S., Lifwarda, & Nindya Satriani, S. N. (2020). Recognition of Image Pattern to Identification of Braille Characters to Be Audio Signals for Blind Communication Tools. IOP Conference Series: Materials Science and Engineering, 846(1). https://doi.org/10.1088/1757-899X/846/1/012008

Singh, G., Kumar, B., & Jassi, J. S. (2015). Odia Braille: Text transcription via image processing. 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE). https://doi.org/10.1109/ABLAZE.2015.7154983

Smelyakov, K., Chupryna, A., Yeremenko, D., Sakhon, A., & Polezhai, V. (2018). Braille Character Recognition Based on Neural Networks. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). https://doi.org/10.1109/DSMP.2018.8478615

Subur, J., Sardjono, T. A., & Mardiyanto, R. (2015). Braille Character Recognition Using Find Contour Method. 5th 2015 International Conference on Electrical Engineering and Informatics (ICEEI). https://doi.org/10.1109/ICEEI.2015.7352588

Zhi, T., Duan, L.-Y., Wang, Y., & Huang, T. (2016). Two-stage pooling of deep convolutional features for image retrieval. 2016 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/ICIP.2016.7532802