TransDocs: Optical Character Recognition with word to word translation
원문 초록
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.
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MT$^{3}$: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning
Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline
Evaluating Menu OCR and Translation: A Benchmark for Aligning Human and Automated Evaluations in Large Vision-Language Models
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation
Development of a WAZOBIA-Named Entity Recognition System
CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents
토론
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