Ollama OCR for Web
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Ollama OCR For Web
Overview :
Ollama-OCR is an optical character recognition (OCR) model based on Ollama that can extract text from images. It leverages advanced visual language models such as LLaVA, Llama 3.2 Vision, and MiniCPM-V 2.6 to provide high-accuracy text recognition. This model is highly useful for scenarios requiring text information extraction from images, such as document scanning and image content analysis. It is open-source, free to use, and easily integrable into various projects.
Target Users :
Target audience includes developers, researchers, and business users who need to extract text from images. For developers, it can be integrated into various applications to implement image text recognition; for researchers, it serves as a powerful tool to study the performance of visual language models on OCR tasks; for business users, it can automate document processing and image content analysis to improve work efficiency.
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Use Cases
Developers can integrate ollama-ocr into their web applications to provide users with image text recognition features, such as online document scanning services.
Researchers can use this model to investigate the OCR performance of visual language models across different image scenarios, advancing related technologies.
Businesses can deploy ollama-ocr to automate the processing of large volumes of image documents, such as invoices and contracts, enhancing data entry efficiency.
Features
Supports various advanced visual language models, including LLaVA, Llama 3.2 Vision, and MiniCPM-V 2.6, offering diverse text recognition capabilities.
Capable of processing single images, multiple images, and video inputs to adapt to different usage scenarios.
Flexible output formats including Markdown, plain text, and JSON, facilitating further processing and application.
Supports deployment and operation in different environments via Docker.
Provides detailed documentation and examples to help users get started quickly.
How to Use
1. Install Ollama.
2. Pull the required models, such as llama3.2-vision:11b, llava:13b, and minicpm-v:8b.
3. Clone the ollama-ocr repository: git clone git@github.com:dwqs/ollama-ocr.git.
4. Navigate to the project directory: cd ollama-ocr.
5. Install dependencies: yarn or npm install.
6. Start the development server: yarn dev or npm run dev.
7. Input images into the model to obtain text output.
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