Abstract
Segmenting Arabic handwritings had been one of the subjects of research in the field of Arabic character recognition for more than 25 years. The majority of reported segmentation techniques share a critical shortcoming, which is over-segmentation. The aim of segmentation is to produce the letters (segments) of a handwritten word. When a resulting letter (segment) is made of more than one piece (stroke) instead of one, this is called over-segmentation. Our objective is to overcome this problem by using an Artificial Neural Networks (ANN) to verify the resulting segment. We propose a set of heuristic-based rules to assemble strokes in order to report the precise segmented letters. Preprocessing phases that include normalization and feature extraction are required as a prerequisite step for the ANN system for recognition and verification. In our previous work [1], we did achieve a segmentation success rate of 86% but without recognition. In this work, our experimental results confirmed a segmentation success rate of no less than 95%.
Highlights
Automatic recognition of handwritings is developing well as a result of research contributions in this field
Most of the work on handwriting recognition was done on Latin text. This lack in Arabic handwriting recognition systems is highly related to the difficulty of segmenting words into characters because of the cursive nature of Arabic handwriting
Comparing the previous methods of segmentation approaches and our approach, this segmentation method is resolved the shortcomings of the previous related methods and achieved better results by avoiding under segmentation. This depended on the high performance of the agents and the right decision to select artificial neural network with grouping rules which improved detecting the candidate segmentation points
Summary
Automatic recognition of handwritings is developing well as a result of research contributions in this field. Several factors contribute to the difficulty of recognizing Arabic handwritings, which is, mainly, attributed to the quality of the writing (the poorer the writing, the harder it is to be recognized) This subject is still under research because of its potential array of applications. Some segments may not be recognized because of the poor writing, so they are combined with the following segment and a new set of twenty features is extracted again and passed to the neural network. This process is repeated until the segment is recognized.
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