A DATASET FOR THE DIACRITICS IMAGES OF THE HOLY QURAN: TOWARDS A TACTILE-VISION SYSTEM FOR THE VISUALLY IMPAIRED (2025)

An Arabic Optical Braille Recognition System

2000

Technology has shown great promise in providing access to textual information for visually impaired people. Optical Braille Recognition (OBR) allows people with visual impairments to read volumes of typewritten documents with the help of flatbed scanners and OBR software. This project looks at developing a system to recognize an image of embossed Arabic Braille and then convert it to text.

Hardware and Software Co-Design of Arabic Alphabets Recognition Platform for Blind and Visually Impaired Persons

The Open Electrical & Electronic Engineering Journal, 2017

Background: Optical character Recognition (OCR) is a technic that converts scanned or printed text images into editable text. Many OCR solutions have been proposed and used for Latin and Chinese alphabets. However not much can be found about OCRs for the handwriting scripts Arabic Alphabets, and especially to be used for blind and visually impaired persons. This paper has been an attempt towards the development of an OCR for Arabic Alphabets dedicated to blind and visually impaired persons. Method: The proposed Optical Arabic Alphabets Recognition algorithm includes binarization of the inputted image, segmentation, feature extraction and a classification based on neural networks to match read Arabic alphabets with trained pattern. The proposed algorithm has been developed using Matlab, and the solution was designed to be implemented on hardware platform and can be customized for mobile phones. Conclusion: The presented method has the benefit that the accuracy of recognition is compa...

Braille is a tactile form of reading and writing for the visually impaired, developed by Louis Braille that uses a system of raised dots. Each Braille "cell' or character contains a combination of up to 6 dots. The paper addresses different approaches used to read, recognize, and translate Braille to English via Optical Dot Recognition algorithms using machine learning and Python. We propose a Digital Image Braille Character Recognition, Extraction & Translation System [DIBCRETS], also known asTACT-EYE, which has been tested and can be used by volunteers at NGOs and other organizations for the blind with limited training.

Arabic Braille Recognition and transcription into text and voice

2010

This paper presents a system for a design and implementation of Optical Arabic Braille Recognition(OBR) with voice and text conversion. The implemented algorithm based on a comparison of Braille dot position extraction in each cell with the database generated for each Braille cell. Many digital image processing have been performed on the Braille scanned document like binary conversion, edge detection, holes filling and finally image filtering before dot extraction. The work in this paper also involved a unique decimal code generation for each Braille cell used as a base for word reconstruction with the corresponding voice and text conversion database. The implemented algorithm achieve expected result through letter and words recognition and transcription accuracy over 99% and average processing time around 32.6 sec per page. using matlab environment.

A fuzzy Classification and Recognition System for Arabic Braille Segmented Characters

2018

Braille documents play undeniable crucial role in the daily lives of low vision and visually-impaired people in current information and Internet era. Braille documents and its characters need to be understandable by vision people as well to motivate bridging the communication gap between visuallyimpaired people and vision people in different professions and work areas. Optical Braille recognition systems aim at automatically converting printed Braille characters into natural language characters. Arabic Braille character recognition systems have witnessed some development in recent years. However, there is a demand for more research contributions in this research area specifically in the character feature extraction and character recognition. This paper introduces a new fuzzy classification system for Arabic Braille characters. The system specifically tries to overcome the difficulties of Arabic Braille character feature extraction and recognition. The system has been evaluated using...

Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Mobile Information Systems, 2021

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 Ana...

Identification of hijaiyah letters using wirelessly controlled arabic braille module

Indonesian Journal of Electrical Engineering and Computer Science, 2020

Among the major issues faced by the visually impaired people is the inability to read, and this is could be addressed by learning braille. However the scarcity of skilled instructors to teach the braille codes to the visually impaired people limits the learning process. This paper presents a psychophysical study on the ability of people with visual challenges to identify hijaiyah letters from a specially developed Arabic braille module. This module consists of six miniature solenoids that are wirelessly controlled via a Bluetooth and an Arduino Uno microcontroller. Five different hijaiyah letters were provided to the participants and were randomly repeated for three times. The participants were required to touch the Arabic braille module and state the hijaiyah letters that were displayed. With priori knowledge and initial familiarization of the braille patterns, 72.9% of the trials showed accuracy between the braille display and the stated hijaiyah letters. 20% of the participants h...

Recognition of Double Sided Amharic Braille Documents

International Journal of Image, Graphics and Signal Processing, 2017

Amharic Braille image recognition into a print text is not an easy task because Amharic language has large number of characters requiring corresponding representations in the Braille system. In this paper, we propose a system for recognition of double sided Amharic Braille documents which needs identification of recto, verso and overlapping dots. We use direction field tensor for preprocessing and segmentation of dots from the background. Gradient field is used to identify a dot as recto or verso dots. Overlapping dots are identified using Braille dot attributes (centroid and area). After identification, the dots are grouped into recto and verso pages. Then, we design Braille cell encoding and Braille code translation algorithms to encode dots into a Braille code and Braille codes into a print text, respectively. With the purpose of using the same Braille cell encoding and Braille code translation algorithm, recto page is mirrored about a vertical symmetric line. Moreover, we use the concept of reflection to reverse wrongly scanned Braille documents automatically. The performance of the system is evaluated and we achieve an average dot identification accuracy of 99.3% and translation accuracy of 95.6%.

Implementation of OCR (Optical Character Recognition) Using Otsu Threshold Method for Detecting Tajweed Qur'an

2020

Qur'an is guidelines for Muslims. Appreciate and interpret the Qur'an requires knowledge and understanding. Reading the Qur'an should be tartil, tartil read here interpreted in accordance with the rules of recitation. The introduction of this recitation until now most still use traditional ways in which the method generally using the guidelines of the textbook or learn direct to those skilled in the field of Science recitation. Students as one of the subjects studied the Qur'an need to understand the Qur'an. In the middle of the development of technology and gadgets, allowing applications that can help in studying recitation. This study will discuss the application of recitation detection using OCR (Optical Character Recognition) and Threshold Otsu algorithm that can be read visually recitation by utilizing the camera features on the gadget. Applications Qur'an recitation detector that uses OCR and Otsu threshold algorithm with test data 200 Data obtained by percentage of 66.5%.

Recognition Of Hand Written Arabic Characters

Applications of Digital Image Processing XI, 1988

Optical Character Recognition (OCR) is the process of recognizing printed or handwritten text on paper documents. This paper proposes an OCR system for Arabic characters. In addition to the preprocessing phase, the proposed recognition system consists mainly of three phases. In the first phase, we employ word segmentation to extract characters. In the second phase, Histograms of Oriented Gradient (HOG) are used for feature extraction. The final phase employs Support Vector Machine (SVM) for classifying characters. We have applied the proposed method for the recognition of Jordanian city, town, and village names as a case study, in addition to many other words that offers the characters shapes that are not covered with Jordan cites. The set has carefully been selected to include every Arabic character in its all four forms. To this end, we have built our own dataset consisting of more than 43.000 handwritten Arabic words (30000 used in the training stage and 13000 used in the testing stage). Experimental results showed a great success of our recognition method compared to the state of the art techniques, where we could achieve very high recognition rates exceeding 99%.