Robust Handwriting Recognition with Limited and Noisy Data

Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that have clearly written text and carefully segmented labels. In this paper, we instead focus on learning handwritten charac...

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Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Pham, Hai, Setlur, Amrith, Dingliwal, Saket, Lin, Tzu-Hsiang, Poczos, Barnabas, Huang, Kang, Li, Zhuo, Lim, Jae, McCormack, Collin, Tam, Vu
Format: Article
Language:English
Subjects:
Computer vision
Handwriting
Handwriting recognition
Machine learning
Segmentation
Online Access:Get full text
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Summary:Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that have clearly written text and carefully segmented labels. In this paper, we instead focus on learning handwritten characters from maintenance logs, a constrained setting where data is very limited and noisy. We break the problem into two consecutive stages of word segmentation and word recognition respectively and utilize data augmentation techniques to train both stages. Extensive comparisons with popular baselines for scene-text detection and word recognition show that our system achieves a lower error rate and is more suited to handle noisy and difficult documents
ISSN:2331-8422