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OCRBench: on the hidden mystery of OCR in large multimodal models
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“benchmarks targeting a single perceptual application, such as OCRBench (Yuliang Liu et al. 2024) and ScreenSpot (K. Li et al. 2025), provide more direct measures of perception but are built top-down: their categories are predefined and populated with questions from a specific application domain.”
From PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models (2026) · cites this paper as Yuliang Liu et al. 2024
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Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · cited 1×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
Hawkins, C., Sorgi, M., & Lock, S. (1985) · Springer London · doi:10.1007/978-1-4471-3519-7 · Cited by 9,160 · cited 1×
“5 [64] Shanghai AI Lab 720 DeepSeek-VL-7B [81] DeepSeek 435 Claude3-Opus [82] Anthropic 694 OmniLMM-12B [83] OpenBMB 420 RekaFlash [74] Reka AI 692 TransCore-M [84] PCI Research 405 InternLM-XComposer2.”
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602 · cited 1×
“Among them, LLaMA [3] is an open-source LLM that matches the performance of GPT-3, followed by Alpaca [4], Vicuna [5], GPT-4-LLM [6] to improve the LLM’s alignment ability to follow human instruction, reporting impressive performance compared with proprietary LLMs.”
Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1516 · Cited by 2,428
Self-citationChartQA: A Benchmark for Question Answering about Charts with Visual and Logical ReasoningPreprint
Masry, A., Long, D. X., Tan, J., Joty, S. R., & Hoque, E. (2022) · Findings · doi:10.48550/arxiv.2203.10244 · Cited by 2,023 · cited 1×
“We assess these LMMs on DocVQA[55], InfographicVQA [56] and ChartQA [57].”
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y., Li, Z., & Duerig, T. (2021) · arXiv · doi:10.48550/arxiv.2102.05918 · Cited by 1,977 · cited 1×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
Self-citationImproved Baselines with Visual Instruction Tuning
Liu, H., Li, C., Li, Y., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02484 · Cited by 1,748 · cited 1×
“Other works, such as [20, 21], also focus on aligning the vision module and LLM for improved multimodal understanding.”
Self-citationToward expert-level medical question answering with large language models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734
Self-citationICDAR 2015 competition on Robust Reading
Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S. K., Bagdanov, A. D., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V., Lu, S.-J., et al. (2015) · IEEE International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2015.7333942 · Cited by 1,622 · cited 2×
“…OCR text recognition datasets, including (1) Regular Text Recognition: IIIT5K [37], SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3) Occlusion Scene Text [46], encompassing weakly…”
The IAM-database: an English sentence database for offline handwriting recognition
Marti, U.-V., & Bunke, H. (2002) · International Journal on Document Analysis and Recognition · doi:10.1007/s100320200071 · Cited by 1,589
Synthetic Data for Text Localisation in Natural Images
Gupta, A., Vedaldi, A., & Zisserman, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.254 · Cited by 1,563
ICDAR 2013 Robust Reading Competition
Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Bigorda, L. G. I., Mestre, S., Mas, J., Mota, D. F., Almazán, J., & Heras, L.-P. D. L. (2013) · IEEE International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2013.221 · Cited by 1,506 · cited 1×
“…Recognition : We evaluate LMM using widely-adopted OCR text recognition datasets, including (1) Regular Text Recognition: IIIT5K [37], SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3)…”
mPLUG-Owl: Modularization Empowers Large Language Models with MultimodalityPreprint
Ye, Q.-H., Xu, H., Xu, G., Ye, J., Yan, M., Zhou, Y., Wang, J., Hu, A., Shi, P.-C., Shi, Y., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2304.14178 · Cited by 1,318 · cited 2×
“Additionally, [22, 23] emphasize modality collaboration for image and text.”
Self-citationShareGPT4V: Improving Large Multi-Modal Models with Better CaptionsPreprint
Chen, L., Li, J., Dong, X.-W., Zhang, P., He, C.-H., Wang, J., Zhao, F., & Lin, D.-H. (2023) · European Conference on Computer Vision · doi:10.48550/arxiv.2311.12793 · Cited by 1,271
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., & Tang, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.26 · Cited by 1,068 · cited 1×
“5 [64] Shanghai AI Lab 639 MMAlaya [93] DataCanvas 223 Phi-3-Vision [94] Microsoft 637 VisualGLM [95] Tsinghua 170 WeMM [96] WechatCV 628 OpenFlamingo v2 [97] UW 149 IDEFICS2-8B [98] Hugging Face 626 PandaGPT-13B [99] Tencent AI Lab 36 Step-1V [100] StepFun 625 Chameleon-30B [101] Meta 27 Table 4:…”
How far are we to GPT-4V? Closing the gap to commercial multimodal models with open-source suites
Chen, Z., Wang, W., Tian, H., Ye, S., Gao, Z., Cui, E., Tong, W., Hu, K., Luo, J., Ma, Z., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4231-5 · Cited by 1,058 · cited 8×
“5-Sonnet [ 70] Anthropic 788 LLaVA-Next-Yi-34B [71] UW–Madison 574 GPT-4o-mini-20240718 [72] OpenAI 785 TextMonkey [30] HUST 561 InternVL2-4B [64] Shanghai AI Lab 784 Monkey-Chat [29] HUST 534 InternVL2-2B [64] Shanghai AI Lab 781 InternLM-XComposer2 [73] Shanghai AI Lab 532 GLM-4v-9B [67] Zhipu AI…”
InfluentialSynthetic Data and Artificial Neural Networks for Natural Scene Text RecognitionPreprint
Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2227 · Cited by 995
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966 · cited 2×
“Flamingo [13] and OpenFlamingo [19] enhance a frozen pretrained LLM by incorporating novel gated cross-attention-dense layers, enabling conditioning on visual inputs.”
Towards VQA Models That Can Read
Singh, A., Natarajan, V., Shah, M., Jiang, Y. L., Chen, X., Batra, D., Parikh, D., & Rohrbach, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00851 · Cited by 951 · cited 1×
“Scene Text-Centric VQA : We test LMMs on STVQA [51], TextVQA [52], OCRVQA [53] and ESTVQA [54].”
Li, J., Li, D., Savarese, S., & Hoi, S. (2023) · arXiv · doi:10.48550/arxiv.2301.12597 · Cited by 908 · cited 1×
“BLIP2 [18] introduces the Querying Transformer (Q-Former) as a means to bridge the gap between vision and language models.”
DocVQA: A Dataset for VQA on Document Images
Mathew, M., Karatzas, D., & Jawahar, C. V. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00225 · Cited by 821 · cited 1×
“We have divided the ESTVQA dataset into ESTVQA(CN) and ESTVQA(EN), which specifically include questions and answers in Chinese or English respectively.”
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningPreprint
Chen, J., Zhu, D., Shen, X., Li, X. Y., Liu, Z., Zhang, P., Krishnamoorthi, R., Chandra, V., Xiong, Y., & Elhoseiny, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.09478 · Cited by 754 · cited 1×
“MiniGPT4V2 [26] uses unique identifiers for different tasks when training the model to better distinguish each task instruction effortlessly.”
Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023) · arXiv · doi:10.48550/arxiv.2304.08485 · Cited by 682 · cited 4×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
InfluentialSelf-citationOpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language ModelsPreprint
Awadalla, A., Gao, I., Gardner, J., Hessel, J., Hanafy, Y., Zhu, W., Marathe, K., Bitton, Y., Gadre, S., Sagawa, S., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2308.01390 · Cited by 674 · cited 1×
“Flamingo [13] and OpenFlamingo [19] enhance a frozen pretrained LLM by incorporating novel gated cross-attention-dense layers, enabling conditioning on visual inputs.”
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesPreprint
Veit, A., Matera, T., Neumann, L., Matas, J., & Belongie, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.07140 · Cited by 657 · cited 1×
“…(1) Regular Text Recognition: IIIT5K [37], SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3) Occlusion Scene Text [46], encompassing weakly occluded scene text (WOST) and heavily…”
GIT: A Generative Image-to-text Transformer for Vision and LanguagePreprint
Wang, J., Yang, Z., Hu, X., Li, L., Lin, K., Gan, Z., Liu, Z., Liu, C., & Wang, L. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.14100 · Cited by 636 · cited 1×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
Self-citationWhat Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., Oh, S. J., & Lee, H. (2019) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2019.00481 · Cited by 608
PaLM-E: An Embodied Multimodal Language Model
Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591 · cited 1×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2306.00890 · Cited by 590 · cited 1×
“Additionally, the scarcity of training data for this specific task further complicates the recognition process for LMMs.”
Self-citationInfographicVQAPreprint
Mathew, M., Bagal, V., Tito, R. P., Karatzas, D., Valveny, E., & Jawahar, C. V. (2021) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.48550/arxiv.2104.12756 · Cited by 573
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
Chng, C.-K., & Chan, C. S. (2017) · IEEE International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2017.157 · Cited by 563 · cited 1×
“…SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3) Occlusion Scene Text [46], encompassing weakly occluded scene text (WOST) and heavily occluded scene text (HOST); (4 LMMs primarily rely on…”
A robust arbitrary text detection system for natural scene images
Risnumawan, A., Shivakumara, P., Chan, C. S., & Tan, C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.008 · Cited by 529 · cited 1×
“…datasets, including (1) Regular Text Recognition: IIIT5K [37], SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3) Occlusion Scene Text [46], encompassing weakly occluded scene text…”
Recognizing Text with Perspective Distortion in Natural Scenes
Phan, T. Q., Shivakumara, P., Tian, S.-X., & Tan, C.-L. (2013) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.76 · Cited by 491 · cited 1×
“…text recognition datasets, including (1) Regular Text Recognition: IIIT5K [37], SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3) Occlusion Scene Text [46], encompassing weakly occluded scene…”
MMBench: Is Your Multi-modal Model an All-around Player?
Liu, Y., Duan, H., Zhang, Y., Li, B., Zhang, S., Zhao, W., Yuan, Y., Wang, J., He, C., Liu, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.06281 · Cited by 487 · cited 1×
“[31, 32] have developed effective systems to assess LMMs. [15, 33, 22] introduces GPT-4 or human evaluation to assess the output of LMMs. MMBench [34] evaluates LMM using multiple-choice questions across various dimensions of ability.”
Self-citationMiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Zhu, D., Chen, J., Shen, X., Li, X., & Elhoseiny, M. (2023) · arXiv · doi:10.48550/arxiv.2304.10592 · Cited by 469
ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction
Huang, Z., Chen, K., He, J.-H., Bai, X., Karatzas, D., Lu, S.-J., & Jawahar, C. (2019) · IEEE International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2019.00244 · Cited by 464
Self-citationFUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
Jaume, G., Ekenel, H. K., & Thiran, J. (2019) · International Conference on Document Analysis and Recognition Workshops (ICDARW) · doi:10.1109/icdarw.2019.10029 · Cited by 459 · cited 1×
“We conduct experiments on SROIE [58], FUNSD [59] and POIE [60].”
mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
Ye, Q., Xu, H., Ye, J., Yan, M., Hu, A., Liu, H., Qian, Q., Zhang, J., & Huang, F. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01239 · Cited by 431 · cited 1×
“Additionally, [22, 23] emphasize modality collaboration for image and text.”
What matters when building vision-language models?Preprint
Laurençon, H., Tronchon, L., Cord, M., & Sanh, V. (2024) · Neural Information Processing Systems · doi:10.48550/arxiv.2405.02246 · Cited by 413
Dong, X.-W., Zhang, P., Zang, Y., Cao, Y.-H., Wang, B., Ouyang, L.-K., Wei, X.-L., Zhang, S.-Y., Duan, H., Cao, M., et al. (2024) · arXiv.org · doi:10.48550/arxiv.2401.16420 · Cited by 401
Florence: A New Foundation Model for Computer Vision
Yuan, L., Chen, D., Chen, Y.-L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.11432 · Cited by 389 · cited 1×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
Self-citationTop-down and bottom-up cues for scene text recognition
Mishra, A., Karteek, A., & Jawahar, C. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247990 · Cited by 359 · cited 1×
“Text Recognition : We evaluate LMM using widely-adopted OCR text recognition datasets, including (1) Regular Text Recognition: IIIT5K [37], SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3)…”
Scene Text Visual Question Answering
Biten, A. F., Tito, R., Mafla, A., Gómez, L., Rusiñol, M., Jawahar, C. V., Valveny, E., & Karatzas, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00439 · Cited by 357 · cited 1×
“The responses generated by LMM often include many explanatory terms, so the exact matching approach or Average Normalized Levenshtein Similarity(ANLS) [36] used in the original dataset are not suitable for evaluating LMM in zero-shot scenarios.”
Towards Expert-Level Medical Question Answering with Large Language Models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.09617 · Cited by 338 · cited 1×
“Additionally, the scarcity of training data for this specific task further complicates the recognition process for LMMs.”
Self-citationBai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.16609 · Cited by 332
Scene Text Recognition with Permuted Autoregressive Sequence Models
Bautista, D., & Atienza, R. (2022) · European Conference on Computer Vision · doi:10.48550/arxiv.2207.06966 · Cited by 332 · cited 3×
“We assess these LMMs on DocVQA[55], InfographicVQA [56] and ChartQA [57].”
Curved scene text detection via transverse and longitudinal sequence connection
Liu, Y., Jin, L., Zhang, S., Luo, C., & Zhang, S. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.02.002 · Cited by 323
Self-citationLMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models
Zhang, K., Li, B., Zhang, P., Pu, F., Cahyono, J. A., Hu, K., Liu, S., Zhang, Y., Yang, J., Li, C., et al. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.51 · Cited by 317 · cited 1×
“[31, 32] have developed effective systems to assess LMMs. [15, 33, 22] introduces GPT-4 or human evaluation to assess the output of LMMs. MMBench [34] evaluates LMM using multiple-choice questions across various dimensions of ability.”
Self-citationMonkey: Image Resolution and Text Label are Important Things for Large Multi-Modal Models
Li, Z., Yang, B., Liu, Q., Ma, Z., Zhang, S., Yang, J., Sun, Y., Liu, Y., & Bai, X. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02527 · Cited by 283 · cited 2×
“…[71] UW–Madison 574 GPT-4o-mini-20240718 [72] OpenAI 785 TextMonkey [30] HUST 561 InternVL2-4B [64] Shanghai AI Lab 784 Monkey-Chat [29] HUST 534 InternVL2-2B [64] Shanghai AI Lab 781 InternLM-XComposer2 [73] Shanghai AI Lab 532 GLM-4v-9B [67] Zhipu AI 776 LLaVA-Next-Vicuna-7B [71]…”
Self-citationOCR-VQA: Visual Question Answering by Reading Text in Images
Mishra, A., Shekhar, S., Singh, A. K., & Chakraborty, A. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00156 · Cited by 281 · cited 1×
“TextVQA dataset comprises 45,000+ questions on 28,000+ images sampled from specific OpenImages dataset categories expected to contain text.”
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingPreprint
Zhang, Y., Zhang, R., Gu, J., Zhou, Y., Lipka, N., Yang, D., & Sun, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.17107 · Cited by 278 · cited 1×
“Additionally, [22, 23] emphasize modality collaboration for image and text.”
Zhang, P., Dong, X., Zang, Y., Cao, Y., Qian, R., Chen, L., Guo, Q., Duan, H., Wang, B., Ouyang, L., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2407.03320 · Cited by 245 · cited 1×
“5 [85] Shanghai AI Lab 686 LLaVA-InternLM2-7B [86] Shanghai AI Lab 402 Qwen-VL-Max [78] Alibaba 684 ShareGPT4V-13B [87] Shanghai AI Lab 398 Gemini-1.”
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
Gan, Z., Li, L., Li, C.-Y., Wang, L.-J., Liu, Z.-C., & Gao, J.-F. (2022) · Foundations and Trends in Computer Graphics and Vision · doi:10.48550/arxiv.2210.09263 · Cited by 237 · cited 1×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
Self-citationBLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
Hu, W., Xu, Y., Li, Y., Li, W., Chen, Z., & Tu, Z. (2023) · AAAI Conference on Artificial Intelligence · doi:10.48550/arxiv.2308.09936 · Cited by 233 · cited 1×
“BLIVA [25] combines instruction-aware and global visual features to capture richer image information.”
Li, B., Fang, G., Yang, Y., Wang, Q.-S., Ye, W., Zhao, W., & Zhang, S.-K. (2023) · arXiv.org · doi:10.48550/arxiv.2304.11633 · Cited by 223 · cited 1×
“Moreover, a study by Li et al. [104], evaluates ChatGPT across seven detailed information extraction tasks.”
CogVLM: Visual Expert for Pretrained Language Models
Wang, W., Lv, Q., Yu, W., Hong, W., Qi, J., Wang, Y., Ji, J., Yang, Z., Zhao, L., Song, X., et al. (2023) · arXiv · doi:10.48550/arxiv.2311.03079 · Cited by 217
Self-citationELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsPreprint
Li, C.-Y., Liu, H., Li, L., Zhang, P.-C., Aneja, J., Yang, J.-W., Jin, P., Lee, Y. J., Hu, H., Liu, Z.-C., et al. (2022) · Neural Information Processing Systems · doi:10.48550/arxiv.2204.08790 · Cited by 215 · cited 1×
“The success of large models has also been extended to the multimodal vision-language space [7], leading to a line of research on large multimodal models (LMM), including contrastive learning [8, 9, 10, 11] and generative modeling [2, 12, 13, 14, 15].”
Self-citationDong, X.-W., Zhang, P., Zang, Y., Cao, Y.-H., Wang, B., Ouyang, L.-K., Zhang, S.-Y., Duan, H., Zhang, W.-W., Li, Y., et al. (2024) · Neural Information Processing Systems · doi:10.48550/arxiv.2404.06512 · Cited by 199
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., & Zhou, J. (2023) · arXiv · doi:10.48550/arxiv.2308.12966 · Cited by 193
From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network
Wang, Y., Xie, H., Fang, S., Wang, J., Zhu, S., & Zhang, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01393 · Cited by 190 · cited 1×
“…Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3) Occlusion Scene Text [46], encompassing weakly occluded scene text (WOST) and heavily occluded scene text (HOST); (4 LMMs primarily rely on semantic understanding to…”
Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2304.03277 · Cited by 183 · cited 1×
“Among them, LLaMA [3] is an open-source LLM that matches the performance of GPT-3, followed by Alpaca [4], Vicuna [5], GPT-4-LLM [6] to improve the LLM’s alignment ability to follow human instruction, reporting impressive performance compared with proprietary LLMs.”
Self-citationMitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
Liu, F., Lin, K., Li, L., Wang, J., Yacoob, Y., & Wang, L. (2023) · arXiv · doi:10.48550/arxiv.2306.14565 · Cited by 149 · cited 1×
“[31, 32] have developed effective systems to assess LMMs. [15, 33, 22] introduces GPT-4 or human evaluation to assess the output of LMMs. MMBench [34] evaluates LMM using multiple-choice questions across various dimensions of ability.”
Generative Multimodal Models are In-Context Learners
Sun, Q., Cui, Y., Zhang, X., Zhang, F., Yu, Q., Wang, Y., Rao, Y., Liu, J., Huang, T., & Wang, X. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01365 · Cited by 147
StrucTexT: Structured Text Understanding with Multi-Modal Transformers
Li, Y., Qian, Y., Yu, Y., Qin, X., Zhang, C., Liu, Y., Yao, K., Han, J., Liu, J., & Ding, E. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475345 · Cited by 136
Self-citationOn the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Wang, X., Liu, Y.-L., Shen, C.-H., Ng, C. C., Luo, C., Jin, L., Chan, C.-S., van den Hengel, A., & Wang, L. (2020) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.2002.10215 · Cited by 135 · cited 1×
“Scene Text-Centric VQA : We test LMMs on STVQA [51], TextVQA [52], OCRVQA [53] and ESTVQA [54].”
Self-citationICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
Zhang, R., Yang, M., Bai, X., Shi, B., Karatzas, D., Lu, S., Jawahar, C. V., Zhou, Y., Jiang, Q., Song, Q., et al. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00253 · Cited by 132
Self-citationERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Peng, Q.-M., Pan, Y.-X., Wang, W., Luo, B., Zhang, Z., Huang, Z.-J., Hu, T., Yin, W., Chen, Y., Zhang, Y., et al. (2022) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2210.06155 · Cited by 114
Syntax-Aware Network for Handwritten Mathematical Expression Recognition
Yuan, Y., Liu, X., Dikubab, W., Liu, H., Ji, Z.-L., Wu, Z.-Q., & Bai, X. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00451 · Cited by 108 · cited 1×
“Handwritten Mathematical Expression Recognition(HMER) : We evaluate on HME100K [61], which consists of 74,502 images for training and 24,607 images for testing, with 245 symbol classes.”
Self-citationTextMonkey: An OCR-Free Large Multimodal Model for Understanding Document
Liu, Y., Yang, B., Liu, Q., Li, Z., Ma, Z., Zhang, S., & Bai, X. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3653415 · Cited by 103 · cited 1×
“Monkey [29] enhanced the LMM’s ability to perceive details at a low cost by the generated detailed caption and a high-resolution model architecture.”
Self-citationVLMEvalKit: An Open-Source ToolKit for Evaluating Large Multi-Modality Models
Duan, H., Yang, J., Qiao, Y., Fang, X., Chen, L., Liu, Y., Dong, X., Zang, Y., Zhang, P., Wang, J., et al. (2024) · Proceedings of the 32nd ACM International Conference on Multimedia · doi:10.1145/3664647.3685520 · Cited by 95
Self-citationFeng, H., Liu, Q., Liu, H., Tang, J., Zhou, W., Li, H., & Huang, C. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4250-y · Cited by 92 · cited 1×
“Docpedia [28] directly processes visual input in the frequency domain rather than the pixel space.”
Feng, H., Wang, Z., Tang, J., Lu, J., Zhou, W., Li, H., & Huang, C. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.11592 · Cited by 86 · cited 1×
“MiniGPT4V2 [26] uses unique identifiers for different tasks when training the model to better distinguish each task instruction effortlessly.”
DePlot: One-shot visual language reasoning by plot-to-table translationPreprint
Liu, F., Eisenschlos, J. M., Piccinno, F., Krichene, S., Pang, C., Lee, K., Joshi, M., Chen, W., Collier, N., & Altün, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.10505 · Cited by 79
Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition
Xie, X.-D., Fu, L., Zhang, Z., Wang, Z.-W., & Bai, X. (2022) · European Conference on Computer Vision · doi:10.48550/arxiv.2208.00438 · Cited by 75
Self-citationOBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Laurençon, H., Saulnier, L., Tronchon, L., Bekman, S., Singh, A., Lozhkov, A., Wang, T., Karamcheti, S., Rush, A. M., Kiela, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.16527 · Cited by 72
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition
Li, B., Yuan, Y., Liang, D.-K., Liu, X., Ji, Z.-L., Bai, J.-F., Liu, W.-Y., & Bai, X. (2022) · European Conference on Computer Vision · doi:10.48550/arxiv.2207.11463 · Cited by 69
Self-citationCambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
Tong, S., Brown, E., Wu, P., Woo, S., Middepogu, M., Akula, S. C., Yang, J., Yang, S., Iyer, A., Pan, X., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.16860 · Cited by 68
ICDAR 2019 Competition on Scene Text Visual Question Answering
Biten, A. F., Tito, R., Mafla, A., Gómez, L., Rusiñol, M., Mathew, M., Jawahar, C. V., Valveny, E., & Karatzas, D. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00251 · Cited by 66 · cited 1×
“Scene Text-Centric VQA : We test LMMs on STVQA [51], TextVQA [52], OCRVQA [53] and ESTVQA [54].”
Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution
Kuang, J., Hua, W., Liang, D.-K., Yang, M.-K., Jiang, D., Ren, B., Zhou, Y., & Bai, X. (2023) · IEEE International Conference on Document Analysis and Recognition · doi:10.48550/arxiv.2305.07498 · Cited by 66 · cited 1×
“We conduct experiments on SROIE [58], FUNSD [59] and POIE [60].”
Self-citationAttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
Kass, D., & Vats, E. (2022) · International Workshop on Document Analysis Systems · doi:10.48550/arxiv.2201.09390 · Cited by 51
He, J., Wang, L., Hu, Y., Liu, N., Liu, H., Xu, X., & Shen, H. T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01785 · Cited by 47 · cited 1×
“Recent work such as the ICL-D3IE [102], introduces an in-context learning framework leveraging large language models like GPT-3 and ChatGPT for document information extraction.”
ICFHR 2014 Competition on Handwritten Digit String Recognition in Challenging Datasets (HDSRC 2014)
Diem, M., Fiel, S., Kleber, F., Sablatnig, R., Saavedra, J. M., Contreras, D., Barrios, J. M., & Oliveira, L. (2014) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2014.136 · Cited by 40 · cited 1×
“To confirm this, we create two datasets: Semantic Text (ST) and Non-Semantic Text (NST) using the IIIT5k dictionary.”
End-to-end scene text recognition using tree-structured models
Shi, C., Wang, C., Xiao, B., Gao, S., & Hu, J. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.03.023 · Cited by 38 · cited 1×
“Text Recognition : We evaluate LMM using widely-adopted OCR text recognition datasets, including (1) Regular Text Recognition: IIIT5K [37], SVT [38], IC13 [39]; (2)Irregular Text Recognition: IC15 [40], SVTP [41], CT80 [42], COCOText(COCO) [43], SCUT-CTW1500 (CTW) [44], Total-Text (TT) [45]; (3)…”
PaliGemma: A versatile 3B VLM for transfer
Beyer, L., Steiner, A., Pinto, A. S., Kolesnikov, A., Wang, X., Salz, D., Neumann, M., Alabdulmohsin, I., Tschannen, M., Bugliarello, E., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.07726 · Cited by 17 · cited 1×
“[64] Shanghai AI Lab 842 PaliGemma-3B-mix-448 [65] Google 614 CongRong [66] CloudWalk 827 Cambrian-13B [63] NYU 610 GLM-4v [67] Zhipu AI 814 MiniCPM-V-2 [68] OpenBMB 605 MiniMonkey-2B [69] HUST 802 Cambrian-34B [63] NYU 591 InternVL2-8B [64] Shanghai AI Lab 794 CogVLM-17B-Chat [67] Zhipu AI 590…”
Journal, I. (2022) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem14709 · Cited by 13 · cited 1×
“Additionally, LLaVA-Med [106] attempts to extend multimodal instruction-tuning to the biomedical domain and demonstrates excellent chat abilities with domain knowledge.”
Qiao, Y., Chen, H., Wang, J., Zhao, S.-S., Chen, Y., Ye, X., Li, Z., Qi, X., Gao, P., & Xie, G.-T. (2021) · arXiv.org · doi:10.48550/arxiv.2106.15332 · Cited by 9
Self-citationCross-Modal Attention Networks with Modality Disentanglement for Scene-Text VQA
Fang, Z., Li, L., Xie, Z., & Yuan, J.-L. (2022) · IEEE International Conference on Multimedia and Expo · doi:10.1109/icme52920.2022.9859666 · Cited by 8
DUBLIN - Document Understanding By Language-Image Network
Aggarwal, K., Khandelwal, A., Tanmay, K., Mohammed, O., Liu, Q., Choudhury, M., Chauhan, H., Som, S., Chaudhary, V., & Tiwary, S. (2023) · arXiv.org · doi:10.48550/arxiv.2305.14218 · Cited by 6
Ovis: Structural Embedding Alignment for Multimodal Large Language Model
Lu, S., Li, Y., Chen, Q.-G., Xu, Z., Luo, W., Zhang, K., & Ye, H.-J. (2024) · arXiv · doi:10.48550/arxiv.2405.20797 · Cited by 5
DUBLIN: Visual Document Understanding By Language-Image Network
Aggarwal, K., Khandelwal, A., Tanmay, K., Mohammed, O. K., Liu, Q., Choudhury, M., Chauhan, H., Som, S., Chaudhary, V., & Tiwary, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.65 · Cited by 3
An Efficient Prototype-Based Model for Handwritten Text Recognition with Multi-loss Fusion
Yu, M., Zhang, H., Yin, F., & Liu, C.-L. (2022) · International Conference on Frontiers in Handwriting Recognition · doi:10.1007/978-3-031-21648-0_28 · Cited by 2
Self-citationOPT: Open Pre-trained Transformer Language Models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.13394
Flagged first, then by citations
16 of the 110 references couldn’t be matched to a known paper.
Research works printed but not linked: 14
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Cited by
Sorted by citations
Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAsPreprint
Microsoft, :, Abouelenin, A., Ashfaq, A., Atkinson, A., Awadalla, H. H., Bách, N., Bao, J., Benhaim, A., Cai, M., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2503.01743 · Cited by 669
Self-citationMMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Yue, X., Ni, Y., Zheng, T., Zhang, K., Liu, R., Zhang, G., Stevens, S., Jiang, D., Ren, W., Sun, Y., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00913 · Cited by 429
Self-citationLLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal TrainingPreprint
An, X., Xie, Y., Yang, K., Zhang, W., Zhao, X., Cheng, Z., Wang, Y., Xu, S., Chen, C., Zhu, D., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2509.23661 · Cited by 302
“5 RL (Fast) Qwen 2.5-VL”
InfluentialSelf-citationV Team, Hong, W., Yu, W., Gu, X., Wang, G., Gan, G., Tang, H., Cheng, J., Qi, J., Ji, J., et al. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2507.01006 · Cited by 606
“From solving scientific problems [67; 32; 34] to developing autonomous agents [21; 62; 43], the demands on VLMs have far surpassed simple visual content perception [31], with an increasing emphasis on advanced reasoning abilities.”
Self-citationTextMonkey: An OCR-Free Large Multimodal Model for Understanding Document
Liu, Y., Yang, B., Liu, Q., Li, Z., Ma, Z., Zhang, S., & Bai, X. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3653415 · Cited by 103
“The selection of test datasets and the formulation of evaluation criteria were carried out in accordance with the methodology described in [10].”
InfluentialSelf-citation
Metadata
- Authors
- Liu, Y., Li, Z., Huang, M., Yang, B., Yu, W., Li, C., Yin, X.-C., Liu, C.-L., Jin, L., & Bai, X.
- Affiliations
- Huazhong University of Science and Technology (CN)
- South China University of Technology (CN)
- Microsoft (United States) (US)
- University of Science and Technology Beijing (CN)
- Corresponding author
- Bai, Xiang
- Venue
- Science China Information Sciences
- Published
- 2024-12
- Volume
- 67
- Issue
- 12
- ISSN
- 1869-1919 (ISSN-L)
- 1674-733X
- OpenAlex
- W4405399726
- Cited by
- 167
- References
- 110
- Citations by year
- 2026: 82 · 2025: 64 · 2024: 16
- FWCI
- 22.68
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Science China Information Sciences (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62225603)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- optical character recognition, large multimodal models, evaluation benchmark, key information extraction, handwritten mathematical expression recognition, multilingual text recognition, GPT-4V, Gemini
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