Publication:
OCR error correction for Vietnamese handwritten text using neural machine translation
OCR error correction for Vietnamese handwritten text using neural machine translation
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Date
2021
Authors
D. Q. Nguyen
A. D. Le
M. N. Phan
P. Kromer
I. Zelinka
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Research Projects
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Abstract
OCR post-processing is an important step for improving the quality of OCR output texts. Long short-term memory (LSTM) is a deep learning model, which has wide-range applications in many domains like time series prediction, natural language processing and speech recognition. In this paper, we propose an OCR error correction model using neural machine translation with bidirectional LSTM networks at syllable level. Vietnamese OCR text dataset for the model evaluation is outputted from an OCR engine based on the attention-based encoder-decoder (AED) model taking input of handwritten text in the benchmark database of the ICFHR 2018 Vietnamese online handwritten text recognition competition. The experimental results show that the proposed model helps decrease the word error rate in the OCR output texts of the above AED model by about 2%. The model performance is also discussed and compared to the other baseline methods in the competition
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Keywords
"Artificial neural networks,
Natural language processing,
Learning models,
Speech recognition"