deepdoctection
doctr
deepdoctection | doctr | |
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8 | 12 | |
2,193 | 3,075 | |
6.2% | 5.7% | |
9.2 | 8.9 | |
5 days ago | about 16 hours ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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deepdoctection
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Show HN: Beyond text splitting – improved file parsing for LLM's
https://github.com/deepdoctection/deepdoctection
Have you tried this ?
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April 2023
DeepDoctection: Document extraction and analysis using deep learning models (https://github.com/deepdoctection/deepdoctection)
- DeepDoctection: Document extraction and analysis using deep learning models
- DeepDoctection
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[D] Can I use ML/AI to read the back panels of electronic components?
deepdoctection/deepdoctection: A Repo For Document AI
doctr
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Show HN: How do you OCR on a Mac using the CLI or just Python for free
https://github.com/mindee/doctr/issues/1049
I am looking for something this polished and reliable for handwriting, does anyone have any pointers? I want to integrate it in a workflow with my eink tablet I take notes on. A few years ago, I tried various models, but they performed poorly (around 80% accuracy) on my handwriting, which I can read almost 90% of the time.
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Show HN: BetterOCR combines and corrects multiple OCR engines with an LLM
Yup! But I'm still exploring options. (any recommendations would be welcomed!) Here are some candidates I'm considering:
- https://github.com/mindee/doctr
- https://github.com/open-mmlab/mmocr
- https://github.com/PaddlePaddle/PaddleOCR (honestly I don't know Mandarin so I'm a bit stuck)
- https://github.com/clovaai/donut - While it's primarily an "OCR-free document understanding transformer," I think it's worth experimenting with. Think I can sort this out by letting the LLM reason through it multiple times (although this will impact performance)
- yesterday got a suggestion to consider https://github.com/kakaobrain/pororo - I don't think development is still active but the results are pretty great on Korean text
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OCR at Edge on Cloudflare Constellation
EasyOCR is a popular project if you are in an environment where you can use run Python and PyTorch (https://github.com/JaidedAI/EasyOCR). Other open source projects of note are PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR) and docTR (https://github.com/mindee/doctr).
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DeepDoctection
Last I checked I saw a grocery bill example using https://github.com/mindee/doctr and was fairly accurate. Bear in mind that was last year, hopefully it got even better or there are other libraries
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Confidential Optical Character Recognition Service With Cape
For its OCR service, Cape uses the excellent Python docTR library. Some of the critical benefits of docTR are its ease of use, flexibility, and matching state-of-the-art performance. The OCR model consists of two steps: text detection and text recognition. Cape uses a pre-trained DB Resnet50 architecture for detection, and for recognition, it uses a MobileNetV3 Small architecture. To learn more about the level of OCR accuracy you can expect for your document, you can consult these benchmarks provided by docTR. As you will see, model performance is very competitive compared to other commercial services.
- 👋 Unstable Diffusion here, We're excited to announce our Kickstarter to create a sustainable, community-driven future.
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Frog: OCR Tool for Linux
There's also DocTR which can do text detection and extraction out of the box.
It's command line driven but can display the detected text as an overlay of the document.
https://github.com/mindee/doctr
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OCRmyPDF: Add an OCR text layer to scanned PDF file
If you want to OCR a document image, modern versions of Tesseract can work well. If you last used it a few years ago, the recognition has improved since due to a new text recognition algorithm that uses modern (deep learning) techniques. Browser demo using a modern version: https://robertknight.github.io/tesseract-wasm/.
OCR processing typically consist of two major steps: detecting/locating words or lines of text on the page, and recognizing lines of text.
Tesseract's text recognition uses modern methods, but the text detection phase is still based on classical methods involving a lot of heuristics, and you may need to experiment with various configuration variables to get the best results. As a result it can fail to detect text if you present it with something other than a reasonably clean document image.
Doctr (https://github.com/mindee/doctr) is a new package that uses modern methods for both text detection and recognition. It is pretty new however and I expect will take more time and effort to mature.
- DocTR: Open-Source OCR Based on TensorFlow or PyTorch
- DocTR: A seamless, high-performing and accessible library for OCR-related tasks
What are some alternatives?
DocumentInformationExtraction - Key Information Extraction From Documents: Evaluation And Generator
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
Flowise - Drag & drop UI to build your customized LLM flow
tesserocr - A Python wrapper for the tesseract-ocr API
CascadeTabNet - This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"
keras-ocr - A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.
PentestGPT - A GPT-empowered penetration testing tool
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox
donut - Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
react-native-tesseract-ocr - Tesseract OCR wrapper for React Native
bark - 🔊 Text-Prompted Generative Audio Model
deep-text-recognition-benchmark - Text recognition (optical character recognition) with deep learning methods, ICCV 2019