LOCR: Location-Guided Transformer for Optical Character Recognition (2024.findings-emnlp)
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| Challenge: | Academic documents are packed with texts, equations, tables, and figures, posing challenges for accurate OCR results. |
| Approach: | They propose a model that integrates location guiding into the transformer architecture during autoregression. |
| Outcome: | The proposed model outperforms existing methods on an original large-scale dataset comprising 53M text-location pairs from 89K academic document pages. |
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| Challenge: | Existing open-source OCR models focus on scientific texts or generic printed English . Nougat is unable to parse tables in PubMed articles . |
| Approach: | They propose to train OCR models for scientific or generic printed English . Nougat is a popular tool for parsing academic documents, but unable to parse PubMed tables . |
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Efficient OCR for Building a Diverse Digital History (2024.acl-long)
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| Challenge: | Current optical character recognition (OCR) systems are poorly extensible to low-resource document collections, as learning a language-vision model requires extensive labeled sequences and compute. |
| Approach: | They propose to model optical character recognition as a character level image retrieval problem using a contrastively trained vision encoder. |
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Building OCR/NER Test Collections (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) is the automatic recognition of spans of text as name mentions. |
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Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods (L18-1)
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| Challenge: | Optical Character Recognition (OCR) can produce a range of errors depending on the quality of the original document. |
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| Challenge: | a new study examines the effectiveness of two types of transformer models for OCR post-correction in early modern Dutch plays. |
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Beyond Atomic Characters: Glyph-Aware Sub-character Alignment for Low-Resource Multilingual OCR (2026.acl-long)
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| Challenge: | Low-resource multilingual OCR models struggle with complex script structures and data scarcity. |
| Approach: | They propose a framework for multilingual character recognition that integrates visual and linguistic backbones with a novel glyph-aware interface. |
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| Challenge: | Various fusion strategies have been explored for integration of large language models into multi-modal systems. |
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EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge (2023.emnlp-demo)
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| Challenge: | Existing OCR engines fail to provide accurate, cost-effective and sample-efficient character recognition for public domain documents. |
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Unsupervised Multi-View Post-OCR Error Correction With Language Models (2021.emnlp-main)
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| Challenge: | Prior work used text generation techniques or redundancy in similar passages for OCR error correction, which is not appropriate in cases of low corpus redundancies or weak document contextual information. |
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Quantifying Character Similarity with Vision Transformers (2023.emnlp-main)
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| Challenge: | Off-the-shelf string matching methods are widely used to link entities across datasets, but they are not available for many settings. |
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