Stochastic Neural Computation II: Soft Competitive Learning
IEEE Transactions on Computers
A multilingual, multimodal digital video library system
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
A Neural-Network-Based Approach to Optical Symbol Recognition
Neural Processing Letters
An effective result-feedback neural algorithm for handwritten character recognition
Neural, Parallel & Scientific Computations
Recognition of Handprinted Bangla Numerals Using Neural Network Models
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Sorting and Recognizing Cheques and Financial Documents
DAS '98 Selected Papers from the Third IAPR Workshop on Document Analysis Systems: Theory and Practice
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Educational Video Understanding: Mapping Handwritten Text to Textbook Chapters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
VLSI implementation of CSFN neural network for pattern recognition application
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
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Over the last five years or so, neural network (NN)-based approaches have been steadily gaining performance and popularity for a wide range of optical character recognition (OCR) problems, from isolated digit recognition to handprint recognition. We present an NN classification scheme based on an enhanced multilayer perceptron (MLP) and describe an end-to-end system for form-based handprint OCR applications designed by the National Institute of Standards and Technology (NIST) Visual Image Processing Group. The enhancements to the MLP are based on (i) neuron activations functions that reduce the occurrences of singular Jacobians; (ii) successive regularization to constrain the volume of the weight space; and (iii) Boltzmann pruning to constrain the dimension of the weight space. Performance characterization studies of NN systems evaluated at the first OCR systems conference and the NIST form-based handprint recognition system are also summarized