TransDocs: Optical Character Recognition with word to word translation
This work addresses document translation issues for researchers and practitioners, but it is incremental as it combines existing methods on a specific dataset.
The paper tackles the problem of inaccurate OCR output by integrating OCR with an LSTM-based seq2seq model with attention for English-to-Spanish document translation, achieving results expressed in BLEU-4 scores.
While OCR has been used in various applications, its output is not always accurate, leading to misfit words. This research work focuses on improving the optical character recognition (OCR) with ML techniques with integration of OCR with long short-term memory (LSTM) based sequence to sequence deep learning models to perform document translation. This work is based on ANKI dataset for English to Spanish translation. In this work, I have shown comparative study for pre-trained OCR while using deep learning model using LSTM-based seq2seq architecture with attention for machine translation. End-to-end performance of the model has been expressed in BLEU-4 score. This research paper is aimed at researchers and practitioners interested in OCR and its applications in document translation.