Abstract
Arabic writer identification and associated tasks are still fresh due to huge variety of Arabic writer's styles. Current research presents a fusion of statistical features, extracted from fragments of Arabic handwriting samples to identify the writer using fuzzy ARTMAP classifier. Fuzzy ARTMP is supervised neural model, especially suited to classification problems. It is faster to train and need less number of training epochs to learn from input data for generalization. The extracted features are fed to Fuzzy ARTMP for training and testing. Fuzzy ARTMAP is employed for the first time along with a novel fusion of statistical features for Arabic writer identification. The entire IFN/ENIT database is used in experiments such that 75% handwritten Arabic words from 411 writers are employed in training and 25% for testing the system at random. Several combinations of extracted features are tested using fuzzy ARTMAP classifier and finally one combination exhibited promising accuracy of 94.724% for Arabic writer identification on IFN/ENIT benchmark database.
Highlights
Handwriting analysis could be characterized as one of the significant characteristics of human beings
This research presents a system for Arabic writer identification that exploresfusion of different statistical features for fuzzy ARTMAP training and testing for Arabic writer identification
Following segmentation, prescribed features are extracted from these graphemes and fed to fuzzy ARTMAP classifier for Arabic writer identification
Summary
Handwriting analysis could be characterized as one of the significant characteristics of human beings. In the same line of research conducted in (Abdi and Khemakhem, 2015; Hannad et al, 2015), extracted texture features from Arabic script fragments taken from IFN/ENIT benchmark database are employed for Arabic writer identification with an accuracy of 90% on 411 writers and 87% on 130 writers test case. Segmentation: Script image segmentation into graphemses Feature extraction, selections and normalized vector set Writer idenification: Training and testing fuzzy ARTMAP classifier Fig. 2: Architecture of proposed system for Arabic writer identification. Statistical features are extracted from entire script images or from their fragments exhibits better accuracy for handwriting recognition, writer identification and writer verification (Fadhil et al, 2016; Al-Turkistani and Saba, 2015) In this line of action, Bertolini et al (2013) exhibited high writer identification rates by using a fusion of two statistical features; Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). The entire process of training, learning and matching is exhibited in Fig. 6 and readers are referred to (Charalampidis et al, 2001; Wong et al, 2015) for indepth study of Fuzzy ARTMAP
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