A Survey on Arabic Character Recognition

Abstract

Off-line recognition of text play a significant role in several application such as the automatic sorting of postal mail or editing old documents. It is the ability of the computer to distinguish characters and words. Automatic off-line recognition of text can be divided into the recognition of printed and handwritten characters. Off-line Arabic handwriting recognition still faces great challenges. This paper provides a survey of Arabic character recognition systems which are classified into the character recognition categories: printed and handwritten. Also, it examines the literature on the most significant work in handwritten text recognition without segmentation and discusses algorithms which split the words into characters.

Arabic Hand-Written Character Recognition Based on Deep Convolutional Neural Networks

“…or statistical features (such as moments, histogram of gray level distribution, …etc.) [11]. These features try to maximize the interclass variability while minimizing the intra-class variability and were fed to a classifier.…”

“…In 2015, Lawgali [11] published a survey about Arabic Character Recognition and none of the algorithms mentioned used deep learning. However, also in 2015, Elleuch [9] introduced an Arabic handwritten character recognition using Deep Belief Neural Networks.…”

Contextual Arabic Handwriting Recognition System using Embedded Training based Hybrid HMM/MLP Models

“…In many other works neural networks in its different applications have been extensively applied to classify characters as part of isolated or continuous handwritten word recognizers [11] This paper focus on the impact of using aembedded training based on a hybrid classifier the motivation for the work on the hybrid HMMs and Artificial neural network models presented here originates from a critical analysis of the state of the art in offline handwritten text recognition [16] [17] [18] our previous work on offline handwriting recognition using HMMs [19] researches and experiences in using hybrid HMM/ANN models for automatic speech recognition [20][21] [22][23] [24]and for online handwriting recognition [25]. All these criteria making hybrid modeling an important factor in order to achieve aneffective and efficient system.…”

Recognition of cursive Arabic handwritten text using embedded training based on HMMs

“…Researches have tried various approaches for text recognition employing various techniques for pre-processing, features extraction and classification (Lawgali, 2015).…”