Supercharge Your OCR Pipelines with Open Models (huggingface.co)

A practical guide has been published to help engineers pick and deploy open-weight vision‑language models (VLMs) for modern OCR and document AI. It surveys a fast-growing ecosystem of open models—many are fine-tuned VLMs like OlmOCR, PaddleOCR‑VL, DeepSeek, Chandra and Qwen3‑VL—highlighting that these systems now do much more than text recognition: they handle handwriting, multi‑script text, math and chemistry notation, tables, charts and image grounding (bounding boxes/anchors), and can output in formats such as DocTag, HTML, Markdown or JSON. Some models support prompt‑based task switching (e.g., convert formula to LaTeX), while others are tuned for specific OCR prompts; model sizes range from <1B to ~9B parameters with most in the 3–7B band. The guide explains how to choose a model by use case (digital reconstruction → layout‑preserving DocTags/HTML; LLM inputs → Markdown and image captions; programmatic analysis → JSON), stresses collecting domain‑representative test data, and compares benchmarks (OmniDocBenchmark, OlmOCR‑Bench, CC‑OCR). It also covers deployment realities: optimized runtimes (vLLM/SGLang), quantization and throughput/cost tradeoffs—example costs cited like ~$178 per million pages on H100 for OlmOCR and 200k+ pages/day on an A100 for DeepSeek. Bottom line: there’s no single best model—evaluate a few open models against your format, language and layout needs, and leverage open datasets and toolchains to reduce cost and improve privacy.