360LayoutAnalysis
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360layoutanalysis
Overview :
360LayoutAnalysis is a series of document analysis models and datasets developed by 360 AI Institute, focusing on document layout analysis – the identification and extraction of text, images, tables, and other elements from scanned document images. This technology is crucial for automating document processing, electronic data exchange, and digitizing historical documents. The model employs deep learning and pattern recognition techniques, leveraging trained datasets to enhance its understanding of document structure, particularly focusing on paragraph annotation to support semantic understanding and information extraction of text.
Target Users :
This product is designed for enterprises and research institutions requiring document automation processing, electronic data exchange, and historical document digitization. It is particularly well-suited for fields demanding high-accuracy document layout analysis and information extraction, such as law, finance, healthcare, and education.
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Use Cases
Automation of legal document organization and information extraction.
Structural analysis and key data extraction of financial research reports.
Digitization and information preservation of historical documents.
Features
Supports vertical domain models for Chinese and English papers, as well as Chinese research reports and a general-purpose model.
Lightweight inference, trained based on yolov8, with a single model size of only 6.23MB.
Chinese paper scenarios include paragraph information, aiding in semantic understanding and information extraction.
Chinese research report and general-purpose scenarios are trained on tens of thousands of high-quality datasets.
Open-source models support commercial use, with a commercial license application available through the official email.
Provides detailed usage instructions and code examples to facilitate user onboarding.
How to Use
1. Download and install the required Python environment and dependency libraries.
2. Obtain the model weight files from the provided download link.
3. Prepare the document images to be predicted.
4. Initialize the YOLO model and load the weights using the provided code example.
5. Set the image path and model path, then call the model for prediction.
6. Adjust the confidence threshold and other parameters as needed to obtain prediction results.
7. Analyze the prediction results to extract text, images, tables, and other information from the document.
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