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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
MixTeX multimodal LaTeX, ZhEn, and, Table OCR. It performs efficient CPU-based inference in a local offline on Windows.
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 # MixTeX - Multimodal LaTeX OCR with CPU Inference [](https://arxiv.org/abs/2406.17148) <a href="https://colab.research.google.com/github/RQLuo/MixTeX/blob/main/MixTex_Demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> [](https://huggingface.co/MixTex/ZhEn-Latex-OCR) [](https://www.youtube.com/watch?v=PqQdQ5xT-vk)  ## Online Demo: https://mineai.top/ MixTeX is an innovative multimodal LaTeX recognition mini-program, independently developed by our team. It performs efficient CPU-based inference in a local offline environment. Whether it's LaTeX formulas, tables, or mixed text, MixTeX can easily recognize them all, supporting both Chinese and English processing. Thanks to powerful technical support and optimized design, MixTeX can run efficiently without GPU resources, suitable for any Windows computer, greatly facilitating user experience.  ## Source code setup: *Make sure that you have read the user agreement before starting.* 1. Navigate to the `mixtexgui` directory: `cd mixtexgui` 2. Create a new Conda environment: `conda create -n mixtex python=3.10.14` 3. Activate the environment: `conda activate mixtex` 4. Run `pip install -r requirements.txt` 5. Build the application with PyInstaller: `pyinstaller mixtex_ui.spec` 6. If possible, could everyone please submit the packaged programs from other platforms to GitHub? Thank you for your support! ## Key Features - **LaTeX Formula Recognition**: Accurately recognizes complex LaTeX mathematical formulas, ensuring mathematical expression accuracy. - **Table Recognition**: Efficiently processes and recognizes various tables, generating corresponding LaTeX table code. - **Mixed Text Recognition**: Simultaneously processes text containing words, formulas, and tables, guaranteeing completeness and accuracy of recognition results. - **Bilingual Support**: Whether Chinese or English, MixTeX achieves high-precision recognition, meeting needs in different language environments. ## Technical Characteristics - **Local Offline Inference**: No internet connection required, ensuring data privacy and security, suitable for high-confidentiality user scenarios. - **Lightweight Design**: The program startup file is only about 50 MB, facilitating quick deployment and startup. - **Efficient Operation**: Although the model file size is 300 MB, causing a slightly slower startup, once loaded, it runs extremely fast, ensuring a smooth user experience. - **No GPU Required**: Runs efficiently on CPU, suitable for all Windows computers without the need for high-end hardware support. ## User Guide 1. **Clipboard Image Recognition**: Users can enable the clipboard function by pressing `Win+V` and copy the image to be recognized to the clipboard. 2. **Screenshot Recognition**: Use the built-in Windows screenshot tool or the screenshot key on the keyboard to capture and directly recognize the screenshot content. ## User As a permanently free software, MixTeX promises continuous optimization while maintaining local offline operation, with no advertisements, providing the best user experience. ## Demo MixTeX excels in recognizing complex text, with particularly good performance in English recognition and comparable results for Chinese. The following image demonstrates MixTeX's recognition capabilities for complex text: (Recommended Image size: 400 x 500)    ## Environment Requirements MixTeX uses LaTeX environment for code conversion.
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Ikko Eltociear Ashimine · Japan
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b8db03af0da9fd63, topic:ocr, name:ocr, desc:ocr
matched fp:b8db03af0da9fd63, topic:onnx
matched fp:b8db03af0da9fd63, topic:deep-learning
matched fp:b8db03af0da9fd63, topic:computer-vision