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myOCR: Optical Character Recognition for Myanmar language with Post-OCR Error Correction
Author(s)
Aung, Thura
Thu, Ye Kyaw
Oo, Myat Noe
Date Issued
January 1, 2024
Type
Conference Paper
Abstract
This paper presents the Myanmar Optical Character Recognition (OCR), named myOCR. It utilizes a synthetic text image dataset with 14 different font styles that contains 25,790 text images. The system includes Convolutional Neural Networks (CNN) for feature extraction, Bidirectional Long-Short Term Memory (BiLSTM) networks for sequence modeling, and Connectionist Temporal Classification (CTC) for decoding, evaluated across various iterations (3,000, 6,000, 9,000) and hidden states (64, 128, 256). Statistical Post-OCR correction methods involve N(3,4,5)-grams and edit distances with the Symmetric Delete Spelling correction algorithm (SymSpell). For Neural Machine Translation-based correction, BiLSTM and Transformer models are employed, while the mT5-base and mBART-50 models are used for LLM-based correction. The best base (optical) model is the model with 9,000 iterations that achieved a chrF++ score of over 97.90 and a Word Error Rate (WER) of 9.18%. Transformer correction improved its chrF++ to 99.31 and reduced the WER to 0.66%.
Citation
19th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2024, 2024