Papers with text recognition
ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)
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| Challenge: | Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent. |
| Approach: | They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video. |
Transferring General Multimodal Pretrained Models to Text Recognition (2023.findings-acl)
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| Challenge: | Existing methods for text recognition rely on large-scale pretraining on human-annotated or synthetic data. |
| Approach: | They propose a method to transfer multimodal pretrained models to text recognition using image captioning. |
| Outcome: | The proposed method outperforms the baselines and achieves state-of-the-art performance in the Chinese text recognition benchmark. |
GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer (2025.emnlp-demos)
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| Challenge: | Bengali is the sixth most spoken language in the world, but handwritten text recognition systems for the language are underdeveloped. |
| Approach: | They propose a Bengali handwritten text recognition system that uses a decoder-only transformer to address the unique challenges of Bengali script. |
| Outcome: | The proposed system significantly improves on existing tokenizers on Bengali script. |
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)
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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou
| Challenge: | Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images. |
| Approach: | They propose to use unified structure learning to boost the performance of MLLMs by encoding structure information into text-rich images. |
| Outcome: | The proposed model achieves state-of-the-art on 10 visual document understanding benchmarks. |
A Workflow for HTR-Postprocessing, Labeling and Classifying Diachronic and Regional Variation in Pre-Modern Slavic Texts (2024.lrec-main)
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Piroska Lendvai, Maarten van Gompel, Anna Jouravel, Elena Renje, Uwe Reichel, Achim Rabus, Eckhart Arnold
| Challenge: | a workflow for classifying diachronic and regional language variation in medieval texts is currently being developed . the workflow is generic or language-agnostic, but can be applied to other historical languages as well. |
| Approach: | They propose a workflow for classifying diachronic and regional language variation in medieval texts . they use handwritten text recognition and manual transcription to obtain the data . |
| Outcome: | The proposed workflow covers HTR-postprocessing, annotating and classifying medieval texts . it is accessible to humanists with limited experience in research data infrastructures, analysis or NLP . |
MT3: A Synergistic Multi-Task RL Framework for Specializing MLLMs in Text Image Machine Translation (2026.acl-long)
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Zhaopeng Feng, Yupu Liang, Shaosheng Cao, Jiayuan Su, Jiahan Ren, Zhijie Zhou, Wenxuan Huang, Jian Wu, Zuozhu Liu
| Challenge: | Text Image Machine Translation (TIMT) is a critical subfield of machine translation . it requires accurate optical character recognition, robust visual-text reasoning, and high-quality translation a challenge . |
| Approach: | They propose a multi-task optimization framework to specialize MLLMs into expert TIMT models. |
| Outcome: | The proposed model outperforms baselines on the latest in-domain MIT-10M benchmark. |
Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation (2026.findings-acl)
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Zhiheng Li, Zongyang Ma, Yuntong Pan, Ziqi Zhang, Xiaolei Lv, Bo Li, Jun Gao, Jianing Zhang, Chunfeng Yuan, Bing Li, Weiming Hu
| Challenge: | Multimodal Large Language Models (MLLMs) are increasingly being deployed as content moderators . however, they exploit the Human-AI capability gap and create adversarial environments . smuggling attacks exploit the human-AI gap and exploit the vulnerability . |
| Approach: | They construct a benchmark to evaluate the vulnerability of MLLMs as content moderators . they identify three root causes: limited capabilities of vision encoders, robustness gap in OCR . |
| Outcome: | The proposed model exploits the Human-AI capability gap and is vulnerable to smuggling attacks. |
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding (2025.findings-acl)
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Ahmed Heakl, Muhammad Abdullah Sohail, Mukul Ranjan, Rania Elbadry, Ghazi Shazan Ahmad, Mohamed El-Geish, Omar Maher, Zhiqiang Shen, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Optical Character Recognition (OCR) is a key component of document processing . Arabic text recognition has complex typographic and calligraphic features . |
| Approach: | They propose a comprehensive Arabic OCR benchmark that fills the gaps in evaluation systems. |
| Outcome: | The proposed benchmark outperforms existing models in Arabic by 60% in the character error rate . the best model achieves only 65% accuracy in PDF-to-Markdown conversion . |
When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs (2025.emnlp-main)
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| Challenge: | Large vision and language models have demonstrated remarkable performance in visual question answering tasks. |
| Approach: | They introduce a framework to optimize L-VLMs by leveraging unlabeled images . they conduct extensive experiments on four diverse VQA benchmarks . |
| Outcome: | The proposed framework improves L-VLMs on four visual question answering benchmarks. |
CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition (2025.emnlp-main)
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| Challenge: | Existing vision-language models are not equipped to read diverse languages and scripts found in historical materials. |
| Approach: | They propose to train an open-weight vision-language model for historical text recognition on CHURRO-DS, the largest historical text-recognition dataset to date. |
| Outcome: | The proposed model outperforms existing vision-language models on CHURRO-DS, the largest historical text recognition dataset to date. |