Multi-layer perceptron (MLP) neural network technique for offline handwritten Gurmukhi character recognition

• Published in: 2014 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 5
• Main Authors: Singh, Gurpreet, Sachan, Manoj
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Artificial neural networks; Character recognition; Digitizing documents; Feed Forward topology; Gurmukhi Script; Handwriting recognition; Image segmentation; MLP; Neurons; Offline recognition; Optical character recognition software; Unicode
• ISBN: 1479939749; 9781479939749
• DOI: 10.1109/ICCIC.2014.7238334

Machine vision researchers are working on the area of recognition of handwritten or printed text from scanned images for the purpose of digitizing documents and for reducing the errorless data entry cost. The classic difficulty of being able to correctly recognize language symbols is the complexity and the irregularity among the pictorial representation of characters due to variation in writing styles, size of symbols etc. Character recognition process depends on, how the input data is given to the system. Input data may be categorized as Online data or Offline data. Both the forms of data input have their own issues. In this paper, we are focusing on the Offline Gurmukhi character recognition from text image. There are lot of complexities associated with Gurmukhi Script. In this paper, we present a technique based on Multi Layer Perceptron (MLP) Neural Network model. Here we consider isolated handwritten Gurmukhi characters for recognition. MLP is used because it uses generalized delta learning rules and easily gets trained in less number of iterations. The proposed method in this paper detect graphical symbols by identifying lines and characters from the image. After that it analyzes the symbols by training the network using feed forward topology for a set of desired unicode characters. We achieve the performance rate of proposed system maximum up to 98.96% for recognition of symbols by using MLP neural network.