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LOCR: Location-Guided Transformer for Optical Character Recognition
March 5, 2024, 2:49 p.m. | Yu Sun, Dongzhan Zhou, Chen Lin, Conghui He, Wanli Ouyang, Han-Sen Zhong
cs.CV updates on arXiv.org arxiv.org
Abstract: Academic documents are packed with texts, equations, tables, and figures, requiring comprehensive understanding for accurate Optical Character Recognition (OCR). While end-to-end OCR methods offer improved accuracy over layout-based approaches, they often grapple with significant repetition issues, especially with complex layouts in Out-Of-Domain (OOD) documents.To tackle this issue, we propose LOCR, a model that integrates location guiding into the transformer architecture during autoregression. We train the model on a dataset comprising over 77M text-location pairs from …
abstract academic accuracy arxiv character recognition cs.ai cs.cl cs.cv documents domain issue location ocr optical optical character recognition recognition tables transformer type understanding
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