HMM Based On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction Based on Fuzzy Set Theory for Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Online Character Recognition Using Eigen-Deformations
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Application of Fuzzy Logic to Online Recognition of Handwritten Symbols
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
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This paper presents an innovative hybrid approach for online recognition of handwritten symbols. The approach is composed of two main techniques. Firstly, fuzzy rules are used to extract a meaningful set of features from a handwritten symbol, and secondly a recurrent neural network uses the feature set as input to recognise the symbol. The extracted feature set is a set of basic shapes capturing what is distinctive about each symbol, thus making the network's classification task easier. We propose a new recurrent neural network architecture, associated with an efficient learning algorithm derived from the gradient descent method. We describe the network and explain the relationship between the network and the Markov chains. The approach has achieved high recognition rates using benchmark datasets from the Unipen database.