PaddleOCR‑VL‑1.5, the latest Baidu release, uses only 0.9 B parameters to achieve 94.5% accuracy on OmniDocBench v1.5, surpassing larger open‑source and commercial OCR models, while offering multi‑task, multi‑language support, lightweight deployment, and detailed performance benchmarks.
Deepseek-OCR leverages a visual‑compression approach, combining DeepEncoder and the DeepSeek3B‑MoE‑A570M decoder, to represent document text with far fewer visual tokens, achieving up to 97% OCR accuracy and surpassing GOT‑OCR2.0 and MinerU2.0 on OmniDocBench, while the article offers a one‑click deployment tutorial.
The article evaluates several VLM‑based OCR models on complex financial statements, comparing speed, layout accuracy, and handling of irregular tables, and concludes that while some models excel in specific aspects, none yet deliver a flawless solution for all scenarios.
This article traces OCR's evolution from early CNN‑LSTM systems to modern multimodal VLMs, analyzes leading open‑source models such as DeepSeek‑OCR, PaddleOCR, and MonkeyOCR, and offers practical guidance for long‑document, academic, and edge‑computing scenarios.
The article surveys the evolution of Visually‑Rich Document Understanding (VRDU), highlighting pioneering Chinese OCR research, the LayoutLM family, recent multimodal model breakthroughs, open‑source toolkits, and practical recommendations for handling diverse document types and tasks.
