camelot
pix2struct
| camelot | pix2struct | |
|---|---|---|
| 1 | 5 | |
| 3,553 | 549 | |
| 1.4% | 3.8% | |
| 0.0 | 4.4 | |
| over 1 year ago | 6 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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camelot
pix2struct
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[D] Is there a good ai model for image-to-text where the images are diagrams and screenshots of interfaces?
Here are a few useful resources you could start with: [Pix2Struct by Google Research](https://github.com/google-research/pix2struct) might be a valuable tool, although it will most likely need some fine-tuning to fit your specifics. You can also find some fine-tuned models on HuggingFace by searching 'pix2struct'. Another option worth considering is [DonutI](https://github.com/clovaai/donut). Like Pix2Struct, fine-tuning likely needed to meet your requirements. Tesseract OCR is another alternative, particularly for handling text. It's primarily designed for pages of text, think books, but with some tweaking and specific flags, it can process tables as well as text chunks in regions of a screenshot. Bit too much tweaking for my taste. As I'm also in search of OCR tools for UI and chart screenshots, so share if you find something else.
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How would you go about driving contextual data from images?
For images with text, if you want to do visual qa, document classification, table/key information extraction, checkout https://huggingface.co/blog/document-ai https://github.com/philschmid/document-ai-transformers https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/README.md
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How do you parse tables in PDF with langchain? Especially, the context which is few lines above and below the table.
https://huggingface.co/blog/document-ai https://github.com/microsoft/table-transformer https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/table/README.md
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Advice building model for web elements/ browsing specific site
It is related to document AI. Recently google has released a model pix2struct. Some of the tasks they considered and datasets they used include:
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Best Pipeline for pre-processing text from PDFs for fine-tuning LLMs
Google has a new model pix2struct that can accept image input(charts, documents, book covers, UI screenshos) and answer questions. https://github.com/google-research/pix2struct
What are some alternatives?
excalibur - A web interface to extract tabular data from PDFs
table-transformer - Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS evaluation metric.
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
pdf2doi - A python library/command-line tool to extract the DOI or other identifiers of a scientific paper from a pdf file.
blackmaria - Python package for webscraping in Natural language
video-subtitle-extractor - 视频硬字幕提取，生成srt文件。无需申请第三方API，本地实现文本识别。基于深度学习的视频字幕提取框架，包含字幕区域检测、字幕内容提取。A GUI tool for extracting hard-coded subtitle (hardsub) from videos and generating srt files.
web2text - Source code for the paper "Web2Text: Deep Structured Boilerplate Removal", full paper @ ECIR'18
document-ai-transformers
BucketStore - A simple library for interacting with Amazon S3.