A System for the Conversion of Digital Gujarati Text-to-Speech for Visually Impaired People

Jariwala, Nikisha; Patel, Bankim

doi:10.1007/978-981-10-6626-9_8

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Braille was transformed in several languages, such as Arabic [ 22 ], Hindi [ 18 , 23 ], Urdu [ 24 , 25 ], Tamil [ 26 ], Kannada [ 27 ], Chinese [ 28 , 29 ], Gujrati [ 30 , 31 ], Sinhala [ 16 ], and ODIA [ 32 ]. Sana et al [ 33 ] proposed a system for converting braille to English character identification.…”

Section: Related Workmentioning

confidence: 99%

Fly-LeNet: A deep learning-based framework for converting multilingual braille images

Al-Salman,

AlSalman

2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

Fly-LeNet: A deep learning-based framework for converting multilingual braille images

Al-Salman,

AlSalman

2024

Heliyon

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“…Braille has been converted into other languages like Urdu [40,41], Arabic [42][43][44], Hindi [19,45], Bengali [46,47] Tamil [22], Sinhala [23], Kannada [48], ODIA [49], Chinese [50,51], Korean [52] and Gujarati [53][54][55].…”

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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“…Jie LI et.al [9] proposed a mechanism for Braille character recognition using HAAR features as support vectors. Braille to text and text to Braille transliteration has been proposed in Gujarati [10] [11][12] [13] and Devanagiri [1] [14]. A tool has been proposed in [14] which can edit and create Devanagiri Unicode text using digital Braille typewriter.…”

Section: Machine Learning Based Braille Transliteration Of Odia Langumentioning

confidence: 99%

Machine Learning Based Braille Transliteration of O dia Language

Jha*,

Parvathi

2020

IJITEE

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Braille transliteration of natural languages is required for providing a better opportunity of learning and creating opportunities of ceceity people. It allows a bigger diaspora of non-blind teachers to have written communication with blind people. The present paper proposes a method of Braille transliteration of Handwritten and printed Odia characters automatically into Braille. The current work proposes a method of Braille transliteration of Handwritten Odia text with industry applicable accuracy. The method first preprocesses the text and then segments it into characters and then uses an SVM classifier trained on HOG features of Odia handwritten characters to predict characters and maps the predicted printable character to its corresponding Braille with a very good accuracy. The method can further be used with text to speech engines to help the blind students use this technique with refreshable Braille having audio facility to listen the same.

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A System for the Conversion of Digital Gujarati Text-to-Speech for Visually Impaired People

Cited by 6 publications

References 7 publications

Fly-LeNet: A deep learning-based framework for converting multilingual braille images

Fly-LeNet: A deep learning-based framework for converting multilingual braille images

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

Machine Learning Based Braille Transliteration of O dia Language

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