A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
Handwritten Character Classification Using Nearest Neighbor in Large Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A Statistical Approach for Handwritten Character Recognition Using Bayesian Filter
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Handwritten Character Recognition Based on BP Neural Network
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 02
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Handwritten character recognition in a particular language is one of the favourite topics for research from two last decades. Image processing and pattern recognition plays a lead role in handwritten character recognition. It is not a easy task to build a program to achieve hundred percent accuracy for handwritten characters because even humans too make mistakes to recognize characters. There are three main steps of handwritten character recognition- Data collection and preprocessing, feature extraction and classification. Data collection includes creating a raw file of handwritten character images. Preprocessing steps are applied to find a normalized binary image of handwritten character which is easy to process in next step. Feature extraction is the process of gathering data of different samples so that on the basis of this data we can classify samples with different features. Feature extraction from preprocessed handwritten character plays the most important role in character recognition. Thus feature extraction stage in handwritten character recognition system has a large scope for researchers. In this paper, we also introduce a new feature extraction method for handwritten characters named Cross-corner. We use the results of some promising feature extraction methods to find the best method for this application.