Recognizing Off-Line Cursive Handwriting
Abstract
We present a system for recognizing off-line, cursive, English text, guided in part by global characteristics (style) of the handwriting. We introduce a new method for segmenting words into letters, based on minimizing a cost function. Segmented letters are normalized with a novel algorithm that scales different parts of a letter separately removing much of the variation in the writing. We use a neural network for letter recognition and use the output of the network as posterior probabilities of letters in the word recognition process. We found that using a hidden Markov Model for word recognition is less successful than assuming an independent process for our small set of test words. In our experiments with several hundred words, written by 7 writers, 96% of the test words were correctly segmented, 52% were correctly recognized, and 70% were in the top three choices.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Yanikoglu and Sandon. "Recognizing Off-Line Cursive Handwriting." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994. doi:10.1109/CVPR.1994.323857Markdown
[Yanikoglu and Sandon. "Recognizing Off-Line Cursive Handwriting." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1994.](https://mlanthology.org/cvpr/1994/yanikoglu1994cvpr-recognizing/) doi:10.1109/CVPR.1994.323857BibTeX
@inproceedings{yanikoglu1994cvpr-recognizing,
title = {{Recognizing Off-Line Cursive Handwriting}},
author = {Yanikoglu, Berrin A. and Sandon, Peter A.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1994},
pages = {397-403},
doi = {10.1109/CVPR.1994.323857},
url = {https://mlanthology.org/cvpr/1994/yanikoglu1994cvpr-recognizing/}
}