只有0.9B的PaddleOCR-VL，却是现在最强的OCR模型。

Core Viewpoint - The article highlights the significant advancements in the OCR (Optical Character Recognition) field, particularly focusing on the PaddleOCR-VL model developed by Baidu, which has achieved state-of-the-art (SOTA) performance in document parsing tasks [2][9][45]. Summary by Sections Introduction to OCR Trends - The term OCR has gained immense popularity in the AI community, especially with the emergence of DeepSeek-OCR, which has revitalized interest in the OCR sector [1][2]. Overview of PaddleOCR-VL - PaddleOCR is not a new project; it has been developed by Baidu over several years, with its origins dating back to 2020. It has evolved into the most popular open-source OCR project, currently leading in GitHub stars with 60K [6][7]. - The PaddleOCR-VL model is the latest addition to this series, marking the first time a large model has been applied to the core of OCR document parsing [9][11]. Performance Metrics - PaddleOCR-VL, with only 0.9 billion parameters, has achieved SOTA across all categories in the OmniDocBench v1.5 evaluation set, scoring 92.56 overall [11][12]. - In comparison, DeepSeek-OCR scored 86.46, indicating that PaddleOCR-VL outperforms it by approximately 6 points [14][15]. Model Architecture and Efficiency - PaddleOCR-VL employs a two-step architecture for efficiency: first, a traditional visual model (PP-DocLayoutV2) performs layout analysis, and then the PaddleOCR-VL model processes smaller, framed images for text recognition [18][20]. - This approach allows PaddleOCR-VL to achieve high accuracy without the need for a larger model, demonstrating that effective solutions can often be more about problem-solving than sheer size [16][20]. Practical Applications and Testing - PaddleOCR-VL has shown impressive results in various challenging scenarios, including processing scanned PDFs, handwritten notes, and complex layouts like academic papers and invoices [22][28][34]. - The model's ability to accurately recognize and extract information from structured documents, such as tables, has been particularly noted as a significant advantage for automating data extraction processes [39][41]. Conclusion and Future Prospects - PaddleOCR-VL is now open-source, allowing users to deploy it locally or use it through various demo platforms [44][45]. - The advancements made by both PaddleOCR-VL and DeepSeek-OCR are recognized as significant contributions to the OCR field, each excelling in their respective areas [45][46].