Feature Extraction Technique for Handwritten Character Recognition using Geometric-Based Artificial Neural Network

Abstract—Automatichandwritten characters recognition is a problem, which is currently gathering a lot of attention. The ability of an efficient processing small handwriting samples, such as those found on cheques and envelopes, is one of the significant driving forces behind this current research. This paper describes a geometry based technique for feature extraction which applies to the segmentation-based word recognition systems.In this methodology, an artificial neural network is trained to identify resemblance and patterns among different handwriting character dataset training samples and user-entered characters. The proposed system extracts the geometric features of character and thereby, forming a characterskeleton.The system generates feature vectors as outputs which are used to train a pattern recognition engine based on Neural Networks which makes the system benchmarked. We acquired an accuracy of 95.2% working on a set of 108 features. The Feature-Extraction methods described in this paper have performed well in classification when fed to the neural network, and pre-processing of the image using edge detection method and normalization technique are the ideal choice for degraded noisy images.

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