HMM-based Arabic handwritten word recognition via zone segmentation

This paper presents a novel approach towards Arabic handwritten word recognition using the zone-wise material. Due to complex nature of the Arabic characters involving issues of overlapping and related issues like touching, the segmentation and recognition is a monotonous main occupation of in Arabic cursive (e.g. Naskha, Riqaa and other comparable scripts written for Holy Quran). To solve the issues of this character segmentation in such cursive, HMM founded on sequence modelling relying on the holistic way. This paper proposes an efficient framework word recognition by segmenting the handwritten word features horizontally into three zones (upper, middle and lower) and then recognise the corresponding zones. The aim of this zone is to minimise the quantity of distinct component classes associated to the total a number of classes in Arabic cursive. As an outcome of this proposed approach is to enhance the recognition performance of the system. The elements of segmentation zone especially in middle zone (baseline), where characters are frequently tender, are recognised using HMM. After the recognition of middle zone, HMM Based in Viterbi forced Alignment is performed to mark the right and left characters in conjoint zones. Next, the residue components, if any, in upper and lower zones are highlighted in a character boundary then the Components are joint with the morphology of the character to achieve the whole word level recognition. Water reservoir- created the main properties that had integrated into the framework to increase the performance of the zone segmentation especially for the upper zone for the character to determine the boundary detection imperfections in segmentation stage. A new sliding window-based feature, named hierarchical Histogram OF-Oriented Gradient (PHOG) is suggested for lower and upper zone recognition. The comparison study with other similar PHOG features and found robust for Arabic handwriting script recognition. An exhaustive experiment is performed of other handwriting using different dataset such IFN / IFNT to evaluate the rate and the recognition performance. The outcome of this experiment, it has been renowned that proposed zone-wise recognition increases accuracy.

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