使用開源模型大幅提升你的 OCR 工作流效率
Original: Supercharge your OCR Pipelines with Open Models
Traditional OCR systems (such as Tesseract) often struggle with complex layouts, multi-column tables, handwriting, and mathematical…
本文探討如何利用開源模型（如 Florence-2、Qwen2-VL 與 Llama-3.2-Vision）替代傳統 OCR 系統。開源 VLM 不僅能精準辨識文字，還能直接輸出 JSON 或 Markdown 等結構化格式，解決複雜排版與表格解析的痛點。透過 Hugging Face 生態系，開發者可以輕鬆部署並微調這些模型，打造高效、低成本且隱私安全的文檔處理 Pipeline。
Traditional OCR systems (such as Tesseract) often struggle with complex layouts, multi-column tables, handwriting, and mathematical formulas, while using proprietary APIs (such as Google Cloud Vision or GPT-4o) comes with high long-term costs and privacy risks. The Hugging Face official blog points out that with the explosion of open-source visual language models (VLMs), now is the perfect time to comprehensively upgrade OCR pipelines using open-source models.
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