Handwriting Recognition in Historical Documents with Multimodal LLM
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912098416590848 |
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| author | Li, Lucian |
| author_facet | Li, Lucian |
| contents | There is an immense quantity of historical and cultural documentation that exists only as handwritten manuscripts. At the same time, performing OCR across scripts and different handwriting styles has proven to be an enormously difficult problem relative to the process of digitizing print. While recent Transformer based models have achieved relatively strong performance, they rely heavily on manually transcribed training data and have difficulty generalizing across writers. Multimodal LLM, such as GPT-4v and Gemini, have demonstrated effectiveness in performing OCR and computer vision tasks with few shot prompting. In this paper, I evaluate the accuracy of handwritten document transcriptions generated by Gemini against the current state of the art Transformer based methods.
Keywords: Optical Character Recognition, Multimodal Language Models, Cultural Preservation, Mass digitization, Handwriting Recognitio |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_24034 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Handwriting Recognition in Historical Documents with Multimodal LLM Li, Lucian Computer Vision and Pattern Recognition There is an immense quantity of historical and cultural documentation that exists only as handwritten manuscripts. At the same time, performing OCR across scripts and different handwriting styles has proven to be an enormously difficult problem relative to the process of digitizing print. While recent Transformer based models have achieved relatively strong performance, they rely heavily on manually transcribed training data and have difficulty generalizing across writers. Multimodal LLM, such as GPT-4v and Gemini, have demonstrated effectiveness in performing OCR and computer vision tasks with few shot prompting. In this paper, I evaluate the accuracy of handwritten document transcriptions generated by Gemini against the current state of the art Transformer based methods. Keywords: Optical Character Recognition, Multimodal Language Models, Cultural Preservation, Mass digitization, Handwriting Recognitio |
| title | Handwriting Recognition in Historical Documents with Multimodal LLM |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.24034 |