Summary of Textmonkey: An Ocr-free Large Multimodal Model For Understanding Document, by Yuliang Liu et al.
TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document
by Yuliang Liu, Biao Yang, Qiang Liu, Zhang Li, Zhiyin Ma, Shuo Zhang, Xiang Bai
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents TextMonkey, a large multimodal model (LMM) designed for text-centric tasks. The approach introduces enhancements in attention mechanisms and incorporates similarity-based token filtering to improve performance. Additionally, the model is expanded to include text spotting and grounding capabilities, as well as positional information in responses, enhancing interpretability. The authors fine-tune the model for screenshot tasks and evaluate its performance on 12 benchmarks, achieving notable improvements of 5.2% in Scene Text-Centric tasks, 6.9% in Document-Oriented tasks, and 2.8% in Key Information Extraction tasks. The paper also outperforms previous models in scene text spotting and sets a new standard on OCRBench, a comprehensive benchmark for document understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TextMonkey is a special kind of computer program that can understand text and images. It’s designed to do well at certain tasks involving text, like recognizing words in pictures or extracting important information from documents. The creators of TextMonkey made it better by giving it new tools, like the ability to spot specific texts in images and understand where those texts are located. They also improved how it handles responses, making them more understandable. To test TextMonkey’s abilities, they used it on many different tasks and saw big improvements. In fact, it did better than other similar models at recognizing text in pictures and understanding documents. |
Keywords
» Artificial intelligence » Attention » Grounding » Token
