Arabic Bank Cheque Words Recognition Using Gabor Features

Arabic cheque processing is one of the important applications of handwriting recognition. The recognition of Arabic Cheque bank is still awaiting lots of work in its constituent stages, which include pre-processing, feature extraction and classification. Several feature extraction methods used to recognize handwritten digits and words. The stroke direction is one important feature of Arabic handwriting which Gabor filter proved its ability to detect this local structural feature. On the other hand, investigating different classifiers can improve the recognition accuracy. In this paper, Gabor features are investigated with ELM and SMO classifiers. Two Arabic Cheque datasets, AHDB and CENPARMI, are used for evaluation. The results from Gabor features with SMO classifier outperform previous studies.This paper was made possible by a QUCP award [QUCP-CENG-CSE-15-16-1] from the Qatar University. The statements made herein are solely the responsibility of the authors

Enhanced structural perceptual feature extraction model for Arabic literal amount recognition

One of the important applications for document recognition is the bank cheque processing, which is known as cheque literal amount. A few studies focused on Arabic bank cheque processing system compared to other systems, such as Latin and Chinese. The Arabic script has a number of characteristics that makes it unique among other scripts. It is known that humans are the best pattern recognisers. As such, the features detected while human reads the script can get better recognition rates. Therefore, proposing human reading inspired features (which are called perceptual features) can overcome the unique technical challenges in Arabic literal amount recognition. In this paper, the enhanced structural perceptual feature extraction model (PFM) has been proposed. Two main groups of features, which are the components and dots features and the loops and characters shapes features were combined to construct the PFM. This model was evaluated on standard Arabic Handwriting DataBase (AHDB) dataset. The PFM results outperformed the results reported in the previous studies

Pixel distribution-based features for offline Arabic Handwritten word recognition

Handwritten word recognition is the ability of a computer to receive and interpret intelligible handwritten input. An important document recognition application is bank cheque processing The Arabic bank cheque processing system has not been studied as much as Latin and Chinese systems. The domain of handwriting in the Arabic scnpt presents unique technical challenges; proposing a model for feature extraction which combines multiple types of features most likely will help to improve the recognition rate. This work proposed a pixel distribution-based features model (PDM) for offline Arabic handwritten word recognition. Two combination levels were used: the first combines different features and the second combination was done by ensemble classifiers. The AHDB dataset was used, and the experimental results showed superior performance when combining multiple features and using multi classifiers