Integration MBHMM and Neural Network for Totally Unconstrained Handwritten Numerals Recognition

Authors:
Lin Dong;Shanpei Wu;Bao-Zong Yuan
Affiliations:
-;-;-
Venue:
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Year:
2000

Citing 3
Cited 0

Digital image processing

Digital image processing
Off-Line Recognition of Totally Unconstrained Handwritten Numerals Using Multilayer Cluster Neural Network

IEEE Transactions on Pattern Analysis and Machine Intelligence
Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

In this paper we present a method of Multi-branch two dimensional HMM (hidden markov model) for handwritten numeral recognition and another method of Neural network for handwrittern numeral recognition and then integrated the two method into a totally unconstrained handwritten numeral recognition system. The system is composed of a horizontal super state multi-branch two dimensional HMM, a vertical super state multi-branch two dimensional HMM and a Neural network. The integrated recognition system recognition rate is higher than the three subsystem. The data base of handwriting digits used in this paper was collected at Beijing Postal Center, the digits was scanned from letters zip code, altogether 4000 hand writing digits, 2000 are used for training set, 2000 are used for testing set. The training set recognition rate is 99.85%, the testing set recognition rate is 98.05%. If we used more complex decision strategy the system performance will be better.