doctr
stablediffusion
doctr | stablediffusion | |
---|---|---|
12 | 108 | |
3,075 | 36,333 | |
5.7% | 1.8% | |
8.9 | 0.0 | |
1 day ago | 26 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
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
stablediffusion
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Generating AI Images from your own PC
With this tutorial's help, you can generate images with AI on your own computer with Stable Diffusion.
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Midjourney
If your PC has a GPU(Nvidia RTX 30series+ recommended) of VRAM more than 4GB then try training your own Stable Diffusion model.
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RuntimeError: Couldn't clone Stable Diffusion.
Command: "git" clone "https://github.com/Stability-AI/stablediffusion.git" "C:\Users\Naveed\Documents\A1111 Web UI Autoinstaller\stable-diffusion-webui\repositories\stable-diffusion-stability-ai"
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What is the currently most efficient distribution of Stable Diffusion?
Automatic11112 and sygil-webui aren't "distributions" of Stable Diffusion. This is a repository with some distributions of Stable Diffusion.
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Reimagine XL: this is just Controlnet with a credit system right?
New stable diffusion finetune (Stable unCLIP 2.1, Hugging Face) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. Comes in two variants: Stable unCLIP-L and Stable unCLIP-H, which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available here.
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Stability AI has released Reimagine XL to make copies of images in one click
This model will soon be open-sourced in StabilityAI’s GitHub.
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What am I doing wrong please?
Another question, if that's ok? Stable Diffusion 2.0 - https://github.com/Stability-AI/stablediffusion - if I wanted to use that, do I follow along their instructions and it will work on the M1 still, or you advise against it?
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Tools For AI Animation and Filmmaking , Community Rules, ect. (**FAQ**)
Stable Diffusion (2D Image Generation and Animation) https://github.com/CompVis/stable-diffusion (Stable Diffusion V1) https://huggingface.co/CompVis/stable-diffusion (Stable Diffusion Checkpoints 1.1-1.4) https://huggingface.co/runwayml/stable-diffusion-v1-5 (Stable Diffusion Checkpoint 1.5) https://github.com/Stability-AI/stablediffusion (Stable Difusion V2) https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main (Stable Diffusion Checkpoint 2.1) Stable Diffusion Automatic 1111 Webui and Extensions https://github.com/AUTOMATIC1111/stable-diffusion-webui (WebUI - Easier to use) PLEASE NOTE, MANY EXTENSIONS CAN BE INSTALLED FROM THE WEBUI BY CLICK "AVAILABLE" OR "INSTALL FROM URL" BUT YOU MAY STILL NEED TO DOWNLOAD THE MODEL CHECKPOINTS! https://github.com/Mikubill/sd-webui-controlnet (Control Net Extension - Use various models to control your image generation, useful for animation and temporal consistency) https://huggingface.co/lllyasviel/ControlNet/tree/main/models (Control Net Checkpoints -Canny, Normal, OpenPose, Depth, ect.) https://github.com/thygate/stable-diffusion-webui-depthmap-script (Depth Map Extension - Generate high-resolution depthmaps and animated videos or export to 3d modeling programs) https://github.com/graemeniedermayer/stable-diffusion-webui-normalmap-script (Normal Map Extension - Generate high-resolution normal maps for use in 3d programs) https://github.com/d8ahazard/sd_dreambooth_extension (Dream Booth Extension - Train your own objects, people, or styles into Stable Diffusion) https://github.com/deforum-art/sd-webui-deforum (Deforum - Generate Weird 2D animations) https://github.com/deforum-art/sd-webui-text2video (Deforum Text2Video - Generate videos from texts prompts using ModelScope or VideoCrafter)
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Is AI technology really the issue?
Stable Diffusion's code : https://github.com/Stability-AI/stablediffusion
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I've never seen a YAML file alongside a .ckpt or .safetensors
But if you want to run a 2.x-based model, you'll need to download the corresponding YAML file (either the standard one – v2-inference-v.yaml – from Github or the one that is distributed with the model, if it requires a special one), rename it to have the same name as the model, and place it in the models folder alongside the model.
What are some alternatives?
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
tesserocr - A Python wrapper for the tesseract-ocr API
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
keras-ocr - A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.
MiDaS - Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox
civitai - A repository of models, textual inversions, and more
react-native-tesseract-ocr - Tesseract OCR wrapper for React Native
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
deep-text-recognition-benchmark - Text recognition (optical character recognition) with deep learning methods, ICCV 2019
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion