Improving Optical Braille Recognition in Pre-processing Stage

Murthy, Vishwanath Venkatesh; Hanumanthappa, M.

doi:10.1109/icsns.2018.8573664

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…This encoder works better than traditional methods for automatic feature extraction from images. An improved Optical Braille recognition system was designed by Vishwanath et al that increases the picture quality of the scanned images [50]. To improve the quality of scanned Braille images Vishwanath et al presented an improved optical Braille recognition system.…”

Section: Braille-to-natural Language Conversion Using Deep Learningmentioning

confidence: 99%

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Shokat

Riaz

Rizvi

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.

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Section: Braille-to-natural Language Conversion Using Deep Learningmentioning

confidence: 99%

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Shokat

Riaz

Rizvi

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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“…For most people, reading is an easy activity to do. Every average person can read if they understand the alphabet (Assefa & Assefa, 2018;Murthy & Hanumanthappa, 2018). However, reading activities are challenging for some people, especially for blind people.…”

Section: Introductionmentioning

confidence: 99%

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

Ridwan,

Purbolingga,

Hanisah

2024

CNAHPC

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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.

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Empowering Accessibility: A Braille Translator App with Enhanced User Experience and Integration of Machine Learning Technologies

Moustafa,

Konsowa,

Waseem

et al. 2024

2024 International Telecommunications Conference (ITC-Egypt)

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Improving Optical Braille Recognition in Pre-processing Stage

Cited by 4 publications

References 7 publications

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

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

Empowering Accessibility: A Braille Translator App with Enhanced User Experience and Integration of Machine Learning Technologies

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