Robust document parsing for real-world, distorted documents.
We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions, including scanning, skew, warping, screen-photography, and illumination, we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model's capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency. Code: https://github.com/PaddlePaddle/PaddleOCR
Handles complex, real-world document distortions effectively.
Introduces new tasks like seal recognition and text spotting.
Achieves state-of-the-art accuracy on benchmark tests.
Relies on high-quality annotations for training.
Extreme distortions may still pose challenges.
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