A feature extraction method for Arabic Offline Handwritten Recognition System using Naïve Bayes classifier

Handwriting recognition in the Arabic language is considered one of the most challenging problems and the accuracies in recognizing still need more enhancements due to the Arabic character’s nature, cursive writing, style, and size of writing in contrast to working with other languages. In this paper, we propose a system for Arabic Offline Handwritten Character Recognition based on Naïve Bayes classifier (NB). Extraction features preceded by divided the image of character into three horizontal and vertical zones and 3x3 zones in one and two dimensions respectively, then classified by Naïve Bayes. The performance of the system proposes evaluated by using the benchmark CENPARMI database reached up to 97.05% accuracy rate. Experimental results confirm a high enhancement inaccuracy rate in comparison with other Arabic Optical Character Recognition systems

Arabic cursive text recognition from natural scene images

© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers

Offline arabic character recognition using genetic approach

Many optical character recognition (OCR) techniques and tools have been developed for plurality of languages. A successful OCR system improves interactivity between humans and computers in many applications such as digitising and recognising written content. With regard to Arabic OCR, the problem of handwriting recognition is challenging because Arabic letters are cursive and shapechangeable depending on their positions. OCR systems have reached nearly perfect acknowledgement of Arabic printed text, yet still in its inception and needs to be greatly improved with handwritten text. Therefore in this study, an approach to recognize Arabic characters based on genetic algorithms (GA) is proposed. The approach requires two separate stages; feature extraction and GA for character recognition development. In the feature extraction stage, six features are detected for each character and denoted as a feature vector of 6 integer numbers. The feature vectors are then utilised in the next stage. Three genetic operators namely selection, crossover and mutation are implemented to search for the similar vectors with the best fitness value to recognise the character. The data used in this study were collected from different resources and stored in a database. It consists of 12,500 printed text words in 50 paragraphs and 15,000 words written by 100 different writers, males and females aged 5 to 60 years. Pre-processing operations are conducted including segmenting paragraphs into lines, segmenting line into words, segmenting words into characters, detecting skeleton, and determining baseline and other horizontal zones. The experimental results have shown that the proposed method has achieved promising accuracy recognition rate with 90.46% for printed text and handwritten characters

An Arabic Optical Braille Recognition System

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. It particularly aims to build fully functional Optical Arabic Braille Recognition system. It has two main tasks, first is to recognize printed Braille cells, and second is to convert them to regular text. Converting Braille to text is not simply a one to one mapping, because one cell may represent one symbol (alphabet letter, digit, or special character), two or more symbols, or part of a symbol. Moreover, multiple cells may represent a single symbol