Braille Identification System Using Artificial Neural Networks

Waleed, Mohammed

doi:10.25130/tjps.v22i2.640

Tikrit J. Pure Sci.

2023

DOI: 10.25130/tjps.v22i2.640

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Braille Identification System Using Artificial Neural Networks

Mohammed Waleed¹

Abstract: The Braille system is a widely used method by the blind to read and write. Information technology revolution is changing the way Braille reading and writing, making it easier to use. All kinds of materials can be put into Braille representation, such as bank statements, bus ticket, maps, and music note. In this paper, an artificial neural networks are designed to identify the number's image from (0-9) in Braille representation system. Networks will be trained and tested to be used for identify the scanne… Show more

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Cited by 2 publications

References 8 publications

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Gesture Recognition Algorithm for Visually Blind Touch Interaction Optimization Using Crow Search Method

Aslam

Samreen

2020

IEEE Access

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Touch screen interaction system is highly demanding for new innovations from the point of visual sensing, as familiar and easily tracing the virtual key board on the screen, three dimensional gesture communications methods, and RFID sensing etc. In spite of the existence of these types of interaction methods, visually impaired people struggle to have easy access to touch screens. The main goal of the research is to overcome the navigation problems that blind people face while interacting with touch screens. In this examination we centered to build up a Braille sketch; a motion put together information technique with respect to contact screen advanced mobile phones for outwardly debilitated individuals. Utilizing Braille codes to perform motions on contact screen makes the outwardly tested people agreeable in light of the fact that Braille is the reason for correspondence. The streamlining procedure is the demonstration of amplifying or limiting a genuine function by systematic choosing input parameters from an accessible pool of parameters and to figure the estimation of function. Here, we accept variables as hand finger motion facilitate, for example, the coordinate values on x and y axises, swipe limit speed, swipe least separation, pixel rate and speed of X and Y. To build the execution of the system structure, we improve by differing shrouded layer and neuron using Crow Search Algorithm (CSO). The ANN with CSA achieves the Optimal Hidden Layer and Neuron (OHLN) to anticipate the right motion yields. These strategies present an answer that will consequently perceive the hand signals so impeded individual can without much of a stretch speak with ordinary individuals. The proposed model will give high precision with ideal execution measurements contrasted with other existing created demonstrate.

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Gesture Recognition Algorithm for Visually Blind Touch Interaction Optimization Using Crow Search Method

Aslam

Samreen

2020

IEEE Access

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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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Braille Identification System Using Artificial Neural Networks

Cited by 2 publications

References 8 publications

Gesture Recognition Algorithm for Visually Blind Touch Interaction Optimization Using Crow Search Method

Gesture Recognition Algorithm for Visually Blind Touch Interaction Optimization Using Crow Search Method

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

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