Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts
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3654811.pdf
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Author(s)
Luo, Queenie
Chuang, Yung-Sung
Date Issued
March 30, 2024
Journal
ACM Transactions on Asian and Low-Resource Language Information Processing
Publisher
ACM
Citation
Luo, Queenie and Chuang, Yung-Sung. 2024. "Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts." ACM Transactions on Asian and Low-Resource Language Information Processing.
Version
Final published version
Abstract
Scholars in the humanities heavily rely on ancient manuscripts to study history, religion, and socio-political structures of the past. Significant efforts have been devoted to digitizing these precious manuscripts using OCR technology. However, most manuscripts have been blemished over the centuries, making it unrealistic for OCR programs to accurately capture faded characters. This work presents the Transformer + Confidence Score mechanism architecture for post-processing Google?s Tibetan OCR-ed outputs. According to the Loss and Character Error Rate metrics, our Transformer + Confidence Score mechanism architecture proves superior to the Transformer, LSTM-to-LSTM, and GRU-to-GRU architectures. Our method can be adapted to any language dealing with post-processing OCR outputs.
Subjects
General Computer Science
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3654811
