ripgrep-all
InvoiceNet
| ripgrep-all | InvoiceNet | |
|---|---|---|
| 45 | 4 | |
| 6,261 | 2,401 | |
| - | - | |
| 7.6 | 3.4 | |
| 14 days ago | 29 days ago | |
| Rust | Python | |
| GNU General Public License v3.0 or later | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ripgrep-all
- Ripgrep-all: rga: ripgrep, but also search PDFs, E-Books, Office documents, zip
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Ripgrep is faster than {grep, ag, Git grep, ucg, pt, sift}
I searched in portage, and it seems there is another version working also with other documents like PDFs and doc.
https://github.com/phiresky/ripgrep-all
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Calibre – New in Calibre 7.0
If you want even faster search across different formats, you can try ripgrep-all ( https://github.com/phiresky/ripgrep-all ). It can search across epub, docx, pdf, zip, mp4 etc. If you are handy with the tool, you can write custom adaptor to search across images using OCR with tesseract.
- Rga: Ripgrep, but also search in PDF, ebooks, office documents, zip, tar.gz etc.
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Show HN: Khoj – Chat Offline with Your Second Brain Using Llama 2
1. If you want better adoption especially among corporations, GPL-3 wont cut it. Maybe think of some business friendly licenses (MIT etc)
2. I understand the excitement about llm's. But how about making something more accessible. I use rip-grep-all (rga) along with fzf [1] that can search all files including pdfs in a specific folders. However, I would like a GUI tool to search across multiple folders, provide priority of results across folders and store and search histories where I can do a meta-search. This is sufficient for 95% of my usecases to search locally and I dont need LLM. If khoj can enable such search as default without LLM that will be a gamechanger for many people without a heavy compute machine or who dont want to use OpenAI.
[1] https://github.com/phiresky/ripgrep-all/wiki/fzf-Integration
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How to make file paths clickable?
I use `rga` to search through multiple PDF files for work. The tool returns a list of files and I would like to make those file paths clickable.
- Burgr – Books in Your Terminal
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Is there a way to searching multiple epub and pdf?
rga, aka ripgrep-all
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Internet Archive Scholar
I wanted to say 'au contrer' to your 'screenshots are not searchable' and link this[0] but I don't actually see images in the readme.. I swear it was there, maybe it's a buried extra flag..
[0] https://github.com/phiresky/ripgrep-all
- Recoll – Full-text search for your desktop
InvoiceNet
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How would you annotate resumes for object detection?
You can also possibly look at invoice extraction tools such as https://github.com/naiveHobo/InvoiceNet. They solve a similar issue and are researched fairly well, since there is a big market for that.
- Pdfsandwich
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Extract informations from invoices with machine learning
Also, I would suggest you to use this codebase: https://github.com/naiveHobo/InvoiceNet
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P Information Extraction From A Document
You can check out this repository. It contains an implementation of some recent research in deep learning for information extraction on invoices. https://github.com/naiveHobo/InvoiceNet
What are some alternatives?
pdfgrep - PDFGrep is a GNU/Emacs module providing grep comparable facilities but for PDF files
GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
OCRmyPDF - OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched
pytorch2keras - PyTorch to Keras model convertor
notational-fzf-vim - Notational velocity for vim.
awesome-document-understanding - A curated list of resources for Document Understanding (DU) topic
fd - A simple, fast and user-friendly alternative to 'find'
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
ripgrep - ripgrep recursively searches directories for a regex pattern while respecting your gitignore
nixpkgs - Nix Packages collection & NixOS
coral-ordinal - Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)