About this paper Cite this paper Kim, G. et al. (2022). OCR-Free Document Understanding Transformer. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13688. Springer, Cha...
In: Proceedings of the IEEE International Conference on Computer Vision, 2021. 14194–14203 Google Scholar Xie X D, Fu L, Zhang Z F, et al. Toward understanding WordArt: corner-guided transformer for scene text recognition. In: Proceedings of the European conference on computer vision, 2022....
By eliminating the need for entering data by hand and reducing paper usage, the OCR program can result in cost savings over time. Digitized documents can be encrypted and protected more effectively than physical copies, enhancing data security. Now, it’s time to learn some of the best OCR ...
S Mihov,P Mitankin,KU Schulz - IEEE Computer Society 被引量: 23发表: 2007年 A High Speed String Correction Method Using a Hierarchical File This paper describes a high speed string correction method using a hierarchical file. After reviewing a string correction method based on the Levenshtein ...
To generate synthetic datasets with our SynthDoG, please see./synthdog/README.mdandour paperfor details. Updates 2023-06-15We have updated all Google Colab demos to ensure its proper working. 2022-11-14New version 1.0.9 is released (pip install donut-python --upgrade). See1.0.9 Release ...
Scanning is performed in one resolution to use the picture corpus in the current and next generation image analysis scheme of the paper. All pictures are stored in the format of TIFF (*.tiff). We use flatbed scanner because OCR’s end-user uses flat bedded Conclusion Thus we propose a ...
Official Implementation of Donut and SynthDoG | Paper | Slide | PosterIntroductionDonut 🍩, Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows ...
This paper presents three probabilistic text retrieval methods designed to carry out a full-text search of English documents containing OCR errors. By sear... M Ohta,A Takasu,J Adachi - 《Research Bulletin of the National Center for Science Information System》 被引量: 41发表: 1997年 GAS mete...
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Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of Large Multimodal Models, such as GPT...