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2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (2017)
Kyoto, Japan
Nov. 9, 2017 to Nov. 15, 2017
ISSN: 2379-2140
ISBN: 978-1-5386-3586-5
pp: 639-645
ABSTRACT
We introduce two data augmentation and normalization techniques, which, used with a CNN-LSTM, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting recognition tasks. (1) We apply a novel profile normalization technique to both word and line images. (2) We augment existing text images using random perturbations on a regular grid. We apply our normalization and augmentation to both training and test images. Our approach achieves low WER and CER over hundreds of authors, multiple languages and a variety of collections written centuries apart. Image augmentation in this manner achieves state-of-the-art recognition accuracy on several popular handwritten word benchmarks.
INDEX TERMS
handwritten character recognition, neural nets, text detection
CITATION

C. Wigington, S. Stewart, B. Davis, B. Barrett, B. Price and S. Cohen, "Data Augmentation for Recognition of Handwritten Words and Lines Using a CNN-LSTM Network," 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, 2018, pp. 639-645.
doi:10.1109/ICDAR.2017.110
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