{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:52:42Z","timestamp":1775487162778,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u201313) (a\u2013m) and class 2 (14\u201326) (n\u2013z) 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.<\/jats:p>","DOI":"10.3390\/s22051836","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods"],"prefix":"10.3390","volume":"22","author":[{"given":"Sana","family":"Shokat","sequence":"first","affiliation":[{"name":"Department of Computer Science and IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7154-0926","authenticated-orcid":false,"given":"Rabia","family":"Riaz","sequence":"additional","affiliation":[{"name":"Department of Computer Science and IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4694-3250","authenticated-orcid":false,"given":"Sanam Shahla","family":"Rizvi","sequence":"additional","affiliation":[{"name":"Raptor Interactive (Pty) Ltd., Eco Boulevard, Witch Hazel Ave, Centurion 0157, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0895-9665","authenticated-orcid":false,"given":"Inayat","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Buner, Buner 19290, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0737-2021","authenticated-orcid":false,"given":"Anand","family":"Paul","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"ref_1","unstructured":"WHO (2019). 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