Eigen-Based offline handwritten digit recognition using Multi-layer perceptron

Faruqi, Faraz and Muralikrishna, S.N. (2017) Eigen-Based offline handwritten digit recognition using Multi-layer perceptron. In: International Conference on Applied Sciences, Engineering and Technology, 10/07/2017, MIT, Manipal.

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Abstract

Offline handwritten digit recognition is one of the important tasks in pattern recognition which is being addressed for several decades. The application of digit recognition lies majorly in areas like postal mail sorting, bank check processing, form data entry etc. In recent years, research in this area focusses on improving the accuracy and speed of the recognition systems. Many algorithms have been proposed that achieve high recognition rates. In this paper, we propose a highly accurate and fast method using an artificial neural network. Also, we bring out a comparative study of Kirsch directional feature versus the Eigen-based method using two classifiers, Multi-Layer Perceptron (MLP) and Multi-class Support Vector Machine (SVM). Experiments were conducted using the famous MNIST dataset. The proposed method shows a recognition rate of 98.6%.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Principal Component Analysis; MNIST dataset; Feature Extraction; Support Vector Machine; Multi-Layer Perceptron
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 16 Aug 2017 03:55
Last Modified: 16 Aug 2017 03:55
URI: http://eprints.manipal.edu/id/eprint/149513

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