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Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (ﺍ), to class 39, Bri Yay (ے). Total (N = 144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N = 5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (1–13), Alif-Zaal (ﺫ - ﺍ), category-2 (14–26), Ray-Fay (ﻒ - ﺮ), and category-3 (27–39), Kaaf-Bri Yay (ے - ﻕ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73% accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]