Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
Abstract
We introduce a new approach for on-line recognition of handwrit(cid:173) ten words written in unconstrained mixed style. The preprocessor performs a word-level normalization by fitting a model of the word structure using the EM algorithm. Words are then coded into low resolution "annotated images" where each pixel contains informa(cid:173) tion about trajectory direction and curvature. The recognizer is a convolution network which can be spatially replicated. From the network output, a hidden Markov model produces word scores. The entire system is globally trained to minimize word-level errors.
Cite
Text
Bengio et al. "Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models." Neural Information Processing Systems, 1993.Markdown
[Bengio et al. "Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/bengio1993neurips-globally/)BibTeX
@inproceedings{bengio1993neurips-globally,
title = {{Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models}},
author = {Bengio, Yoshua and LeCun, Yann and Henderson, Donnie},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {937-944},
url = {https://mlanthology.org/neurips/1993/bengio1993neurips-globally/}
}