| Autor: |
Fu, Ling, Yang, Biao, Kuang, Zhebin, Song, Jiajun, Li, Yuzhe, Zhu, Linghao, Luo, Qidi, Wang, Xinyu, Lu, Hao, Huang, Mingxin, Li, Zhang, Tang, Guozhi, Shan, Bin, Lin, Chunhui, Liu, Qi, Wu, Binghong, Feng, Hao, Liu, Hao, Huang, Can, Tang, Jingqun, Chen, Wei, Jin, Lianwen, Liu, Yuliang, Bai, Xiang |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest recently. Existing benchmarks have highlighted the impressive performance of LMMs in text recognition; however, their abilities on certain challenging tasks, such as text localization, handwritten content extraction, and logical reasoning, remain underexplored. To bridge this gap, we introduce OCRBench v2, a large-scale bilingual text-centric benchmark with currently the most comprehensive set of tasks (4x more tasks than the previous multi-scene benchmark OCRBench), the widest coverage of scenarios (31 diverse scenarios including street scene, receipt, formula, diagram, and so on), and thorough evaluation metrics, with a total of 10,000 human-verified question-answering pairs and a high proportion of difficult samples. After carefully benchmarking state-of-the-art LMMs on OCRBench v2, we find that 20 out of 22 LMMs score below 50 (100 in total) and suffer from five-type limitations, including less frequently encountered text recognition, fine-grained perception, layout perception, complex element parsing, and logical reasoning. The benchmark and evaluation scripts are available at https://github.com/Yuliang-liu/MultimodalOCR. |
| Databáze: |
arXiv |
| Externí odkaz: |
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