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Olmocr 7B 0225 Preview
olmOCR-7B-0225-preview
O
Olmocr 7B 0225 Preview
OCROther categories#Document Recognition#Text Generation#Image Processing#AI Model#Productivity ToolStandard PicksOpen Source
Overview :
olmOCR-7B-0225-preview is an advanced document recognition model developed by the Allen Institute for AI. It aims to rapidly convert document images into editable plain text through efficient image processing and text generation techniques. Fine-tuned from Qwen2-VL-7B-Instruct, it combines powerful visual and language processing capabilities, suitable for large-scale document processing tasks. Its key advantages include high processing efficiency, accurate text recognition, and flexible prompt generation. This model is intended for research and educational use, is licensed under the Apache 2.0 license, and emphasizes responsible use.
Target Users :
This model is designed for users who need to efficiently process document images and extract text, such as researchers, educators, data analysts, and businesses requiring automated document processing. It rapidly converts scanned documents or images into editable text, significantly improving workflow efficiency.
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Use Cases
Convert scanned academic paper images into editable plain text for subsequent editing and citation.
Extract text content from historical document images for digital preservation and research.
Process business contract images to quickly extract key information and generate text records.
Features
Supports single-page document image input with a maximum edge length of 1024 pixels.
Generates high-quality text output incorporating document metadata.
Provides a manual prompt generation method for user customization.
Supports batch processing for efficient handling of large-scale documents.
Compatible with various document formats, including PDF and image files.
How to Use
1. Install the olmOCR toolkit: Use `pip install olmocr`.
2. Prepare the document image: Render the target document as an image with a maximum edge length of 1024 pixels.
3. Construct the prompt: Use the methods within the olmOCR toolkit to extract document metadata and generate a prompt.
4. Load the model: Load the pre-trained model using the Transformers library.
5. Input image and prompt: Pass the image and prompt to the model for inference.
6. Obtain output: The model generates text output; decode and extract the results.
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