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Doclayout YOLO
Overview :
DocLayout-YOLO is a deep learning model designed for document layout analysis, enhancing accuracy and processing speed through diverse synthetic data and global-to-local adaptive perception. The model utilizes the Mesh-candidate BestFit algorithm to generate a large and diverse DocSynth-300K dataset, significantly improving fine-tuning performance across different document types. Additionally, it introduces a globally controllable perception field module to better handle multi-scale variations of document elements. DocLayout-YOLO performs exceptionally well on various downstream datasets, showcasing significant advantages in both speed and accuracy.
Target Users :
The primary audience includes researchers and developers in the fields of document processing, document analysis, and pattern recognition. The efficiency and accuracy of DocLayout-YOLO make it an ideal choice for handling large volumes of document data, especially in scenarios where fast and precise document layout analysis is required.
Use Cases
Researchers use DocLayout-YOLO for automated layout analysis of historical texts to support digital archiving efforts.
Businesses adopt this model to enhance the efficiency of automated document processing, reducing the costs of manual proofreading.
Developers integrate DocLayout-YOLO into their document management systems to provide more accurate document content extraction capabilities.
Features
Utilizes the Mesh-candidate BestFit algorithm for document synthesis, generating diverse datasets.
Features a globally controllable perception field module that effectively handles multi-scale variations of document elements.
Fine-tunes the model across various document types to enhance its generalization capabilities.
Offers both online demos and local development options to facilitate quick user experience and deployment.
Supports predictions via scripts or SDKs, accommodating different application scenarios.
Provides downloadable pre-trained models, allowing users to quickly initiate document layout analysis tasks.
Supports PDF content extraction, broadening the model's scope of application.
How to Use
1. Environment Setup: Create and activate a Python virtual environment according to the instructions on the project page, and install the necessary dependencies.
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2. Download Model: Download the pre-trained model files from the provided link.
3. Prepare Data: Prepare the relevant dataset according to the type of documents you wish to analyze.
4. Make Predictions: Use the provided scripts or SDK to load the model and make predictions on new document images.
5. Analyze Results: Review the model's predicted results and perform post-processing or analysis as needed.
6. Fine-tune Model: If necessary, fine-tune the model on specific datasets to improve accuracy.
7. Integration and Deployment: Integrate the trained model into actual application systems for document layout analysis tasks.
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