Robust Handwriting Recognition with Limited and Noisy Data

• URL: http://arxiv.org/abs/2008.08148v1
• Date: Tue, 18 Aug 2020 20:33:23 GMT
• Title: Robust Handwriting Recognition with Limited and Noisy Data
• Authors: Hai Pham, Amrith Setlur, Saket Dingliwal, Tzu-Hsiang Lin, Barnabas Poczos, Kang Huang, Zhuo Li, Jae Lim, Collin McCormack, Tam Vu
• Abstract summary: We 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. Our system achieves a lower error rate and is more suited to handle noisy and difficult documents.
• Score: 7.617456558732551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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