Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

TL;DR

This paper introduces a diagonal-based feature extraction method for handwritten alphabet recognition using neural networks, achieving higher accuracy than traditional horizontal and vertical methods.

Contribution

The paper presents a novel diagonal feature extraction technique that improves recognition accuracy in handwritten alphabet systems using neural networks.

Findings

Higher recognition accuracy compared to conventional methods

Effective for converting handwritten documents into structured text

Suitable for recognizing handwritten names

Abstract

An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and 570 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.