A review of feature extraction techniques for handwritten Arabic text recognition

Abstract

Research in Arabic handwritten recognition has been of growing interest in the last few decades. This is mainly due to its broad spectrum of applications in different fields such as bank check processing, form data entry, postal mail sorting, automatic processing of old manuscripts, etc. In the literature, numerous techniques have been proposed for feature extraction and applied to various types of images. This work provides a comprehensive review of these methods for off-line handwritten Arabic text recognition. It also presents recognition rates and descriptions of the databases used for the discussed approaches. This paper includes background on the field, discussion of feature extraction methods, and future research directions.

Handwritten Arabic Text Recognition using Principal Component Analysis and Support Vector Machines

“…The performance evaluation outcomes spotlight reliability of the combination of the SVM and Ridgelet tools for recognition of the handwritten Arabic words. EI Qacimy et al [11] suggested offline, word-based system for recognition of the handwritten Arabic text depending on the DCT features and a SVM classifier that is improved by reject option. This system comprised four key processes, namely, preprocessing, segmentation into sub-words, extraction based on DCT features, and classification by the SVM RBF classifier.…”

“…However, it may be generalized easily to the multi-class classification problems. The fundamental form of the linear SVM classifier attempts to discovery the optimum hyperplane that separate the best set of samples that belong to differing classes [11]. www.ijacsa.thesai.org…”

“…These characteristics may be categorized into three classes: (i) high-level characteristics, which are drawn from the entire image of the text or word, (ii) medium-level characteristics that are derived from the characters, and (iii) low-level characteristics, which are usually extracted from the related sub-characters [18]. In other respects, the handwritten Arabic text may be recognized using various classifiers like the Support Vector Machines (SVM), Hidden Markov Model (HMM), the k-nearest neighbors (kNN), and the Artificial Neural Network (ANN) classifiers [11,12,26].…”

Arabic Hand Written Character Recognition Based on Contour Matching and Neural Network

“…El Qacimy et al in (El Qacimy, Hammouch & Kerroum, 2015), provided a brief review for the feature extraction methods to the offline handwritten Arabic text recognition in addition to presenting both description for the used database and the recognition rate in the proposed approaches in this study. However, it will be such a quite profit to the research discussion and methodology because it includes a fully discussion for the methods of feature extraction that will help in the contour extraction section in our research.…”

Isolated Arabic Hand Written Letters Recognition Based on Contour Matching and Neural Network

“…El Qacimy et al (2015), provided a brief review for the feature extraction methods to the offline handwritten Arabic text recognition in addition to presenting both description for the used database and the recognition rate in the proposed approaches in this study. However, it will be such a quite profit to the research discussion and methodology because it includes a fully discussion for the methods of feature extraction that will help in the contour extraction section in our research.…”