Feature Extraction Techniques for Marathi Character Classification using Neural Networks Models

Hand written Marathi Character Recognition is challenges to the researchers due to the complex structure. This paper presents a novel approach for recognition of unconstrained handwritten Marathi characters. The recognition is carried out using multiple feature extraction methods and classification scheme. The initial stages of feature extraction are based upon the pixel value features and the classification of the characters is done according to the structural parameters into 44 classes. The final stage of feature extraction makes use of the zoning features. First Pixel values are used as features and these values are further modified as another set of features. All these features are then applied to neural network for recognition. A separate neural network is built for each type of feature. The average recognition rate is found to be 67.96% , 82.67%,63,46% and 76.46% respectively for feed forward , radial basis , elman and pattern recognition neural networks for handwritten marathi characters

Recognition techniques for online Arabic handwriting recognition systems

Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed

Recognition of off-line arabic handwritten dates and numeral strings

In this thesis, we present an automatic recognition system for CENPARMI off-line Arabic handwritten dates collected from Arabic Nationalities. This system consists of modules that segment and recognize an Arabic handwritten date image. First, in the segmentation module, the system explicitly segments a date image into a sequence of basic constituents or segments. As a part of this module, a special sub-module was developed to over-segment any constituent that is a candidate for a touching pair. The proposed touching pair segmentation submodule has been tested on three different datasets of handwritten numeral touching pairs: The CENPARMI Arabic [6], Urdu, and Dari [24] datasets. The final recognition rates of 92.22%, 90.43%, and 86.10% were achieved for Arabic, Urdu and Dari, respectively. Afterwards, the segments are preprocessed and sent to the classification module. In this stage, feature vectors are extracted and then recognized by an isolated numeral classifier. This recognition system has been tested in five different isolated numeral databases: The CENPARMI Arabic [6], Urdu, Dari [24], Farsi, and Pashto databases with overall recognition rates of 97.29% 97.75%, 97.75%, 97.95% and 98.36%, respectively. Finally, a date post processing module is developed to improve the recognition results. This post processing module is used in two different stages. First, in the date stage, to verify that the segmentation/recognition output represents a valid date image and it chooses the best date format to be assigned to this image. Second, in the sub-field stage, to evaluate the values for the date three parts: day, month and year. Experiments on two different databases of Arabic handwritten dates: CENPARMI Arabic database [6] and the CENPARMI Arabic Bank Cheques database [7], show encouraging results with overall recognition rates of 85.05% and 66.49, respectively

A review on handwritten character and numeral recognition for Roman, Arabic, Chinese and Indian scripts

Abstract -There are a lot of intensive researches on handwritten character recognition (HCR) for almost past four decades. The research has been done on some of popular scripts such as Roman, Arabic, Chinese and Indian. In this paper we present a review on HCR work on the four popular scripts. We have summarized most of the published paper from 2005 to recent and also analyzed the various methods in creating a robust HCR system. We also added some future direction of research on HCR