Braille Document Recognition in Southern Indian Languages– A Review

Devi, G. Gayathri; Sathyanarayanan, G.

doi:10.1109/aeeicb.2018.8480950

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…An educational application named mBraille was designed for learning English and Bengali [38]. Braille-to-text conversion is also performed in other languages such as Hindi, Tamil, Urdu, Odia, Arabic, Chinese, Bengali, Telugu, Malayalam, and Kannada [18,[39][40][41][42][43][44][45].…”

Section: Braille Input Mechanismmentioning

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 Input Mechanismmentioning

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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“…Braille is written by punching dots on paper and read by gliding the fingers over the raised dots. A Braille cell is defined by combining six dot patterns of 3 × 2 matrixes [26]. A couple of decades ago, efforts began to develop machines that could assist and speed up the writing process.…”

Section: Literature Reviewmentioning

confidence: 99%

Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods

Shokat

Riaz

Rizvi

et al. 2022

Sensors

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Braille is used as a mode of communication all over the world. Technological advancements are transforming the way Braille is read and written. This study developed an English Braille pattern identification system using robust machine learning techniques using the English Braille Grade-1 dataset. English Braille Grade-1 dataset was collected using a touchscreen device from visually impaired students of the National Special Education School Muzaffarabad. For better visualization, the dataset was divided into two classes as class 1 (1–13) (a–m) and class 2 (14–26) (n–z) using 26 Braille English characters. A position-free braille text entry method was used to generate synthetic data. N = 2512 cases were included in the final dataset. Support Vector Machine (SVM), Decision Trees (DT) and K-Nearest Neighbor (KNN) with Reconstruction Independent Component Analysis (RICA) and PCA-based feature extraction methods were used for Braille to English character recognition. Compared to PCA, Random Forest (RF) algorithm and Sequential methods, better results were achieved using the RICA-based feature extraction method. The evaluation metrics used were the True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy, Area Under the Receiver Operating Curve (AUC) and F1-Score. A statistical test was also performed to justify the significance of the results.

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“…It also focuses on the technique used for the transliteration of Braille Character to Gujarati Text [6]. Gayathri discussed research work projected earlier for recognition the Southern Indian braille script from a Braille document [7]. An assistive system which separates a scanned document image into text and its conversion to audio, such application presented by Akshay Sharma for blind people.…”

Section: Literature Reviewmentioning

confidence: 99%

Template-Based Transliteration of Braille Character to Gujarati Text—The Application

Vyas

Kumar

2020

Rising Threats in Expert Applications and Solutions

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The digital revolution has not reached everybody within the same manner. During a world wherever consecutive nice invention is anticipated to look on itinerant screens, blind and visually impaired individuals are left behind. However, we have a tendency to try to take advantage of the potential of recent technologies to form their life easier. Making new documents from scratch when visually handicap person embarks on a brand new document will waste valuable time and cash. Instead, we recommend specializing in internal templates which will be utilized for a range of applications. Proposed solution includes three parameters: at initial stage load predefine templates then start digital communication using technology to provide alternative means of reading and writing. Next, technology can help in the production of digital content converted from Braille to Gujarati or vice versa. A third factor for technology is to provide improved access to information like import, export and saving the digital content. Proposed application reduce the efforts to enter text, hence, can be used as stepping stone to identify the challenges that might be faced at the time of Braille Transliteration.

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Braille Document Recognition in Southern Indian Languages– A Review

Cited by 4 publications

References 3 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

Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods

Template-Based Transliteration of Braille Character to Gujarati Text—The Application

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