Introduction to the theory of neural computation
Introduction to the theory of neural computation
Recognition of Handwritten Cursive Arabic Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network Based Offline Tamil Handwritten Character Recognition System
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
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Neural Networks are being used for character recognition from last many years but most of the works were reported to English character recognition. Character recognition is one of the applications of pattern recognition, which has enormous scientific and practical interest. Many scientific efforts have been dedicated to pattern recognition problems and much attention has been paid to develop recognition system that must be able to recognize a character. The main driving force behind neural network research is the desire to create a machine that works similar to the manner our own brain works. Neural networks have been used in a variety of different areas to solve a wide range of problems. A very little work has been reported for Handwritten Hindi Character recognition. In this paper, we have implemented Gradient feature extraction technique, which provides more than 94% recognition accuracy. We have acquired 1000 samples of handwritten Hindi characters by initializing the mouse in graphics mode. The 500 samples have been used for training the network (Train Data) and remaining 500 samples have been used for testing the network (Test Data). The system has been trained using several different forms of handwritings provided by both male and female participants of different age groups. Finally, this rigorous training results an automatic HCR system using MLP network. The error backpropagation algorithm has been used to train the MLP network. A comparative analysis was performed by implementing both global input and Gradient feature input. We have concluded that gradient feature extraction technique provides better recognition accuracy with reduced training time.