datalab-to/surya-ocr-2.5 · Hugging Face
Surya is a 689M param OCR model with these features:
- Accuracy - scores 85.9 on OmniParseBench, the highest of the open models under 1B parameters we have scored, and 83.7% on olmOCR-bench
- Speed - throughput of 4.5 pages/s on one H100 at 192 DPI, 1.7x Surya 2 with MTP (see Throughput)
- Multilingual - scores 74.9% on an internal benchmark set of 91 languages (more here)
- Layout analysis (table, image, header, etc.) with reading order
- Tables as HTML, with merged cells as colspan / rowspan (row and column geometry needs a Surya 2 checkpoint; see Limitations)
Commercial usage
The Surya code is licensed under Apache 2.0. The model weights use a modified AI Pubs Open Rail-M license (free for research, personal use, and startups under $5M funding/revenue). For broader commercial licensing of the model weights, visit our pricing page here.
Limitations
- This is specialized for document OCR. Performance on photos or natural scenes is not the goal.
- Layout / OCR / table_rec all need a running inference backend (vllm or llama.cpp). Detection runs purely on torch and works without it.
- Surya 2.5 reads tables as HTML (full-page OCR, block mode, surya_table), but not row and column geometry: surya_table --mode simple returns an error. Use a Surya 2 checkpoint (SURYA_MODEL_CHECKPOINT=datalab-to/surya-ocr-2) for geometry.
Github : datalab-to/surya
huggingface.co
29 points · 14 comments
Mentions
Surya 10月29日更新了文档识别模型，官方在单张 H100 上测得，每秒处理页数是上一代的1.7倍。 给 AI 整理扫描件可以看它，但别把代码许可套给权重：代码用 Apache-2.0，权重另有商用条件，超出免费范围要另买许可。 datalab-to/surya
datalab-to/surya-ocr-2.5 · Hugging Face Surya is a 689M param OCR model with these features: - Accuracy - scores 85.9 on OmniParseBench, the highest of the open models under 1B parameters we have scored, and 83.7% on olmOCR-bench - Speed - throughput of 4.5 pages/s on one H100 at 192 DPI, 1.7x Surya 2 with MTP (see Throughput) - Multilingual - scores 74.9% on an internal benchmark set of 91 languages (more here) - Layout analysis (table, image, header, etc.) with reading order - Tables as HTML, with merged cells as colspan / rowspan (row and column geometry needs a Surya 2 checkpoint; see Limitations) Commercial usage The Surya code is licensed under Apache 2.0. The model weights use a modified AI Pubs Open Rail-M license (free for research, personal use, and startups under $5M funding/revenue). For broader commercial licensing of the model weights, visit our pricing page here. Limitations - This is specialized for document OCR. Performance on photos or natural scenes is not the goal. - Layout / OCR / table_rec all need a running inference backend (vllm or llama.cpp). Detection runs purely on torch and works without it. - Surya 2.5 reads tables as HTML (full-page OCR, block mode, surya_table), but not row and column geometry: surya_table --mode simple returns an error. Use a Surya 2 checkpoint (SURYA_MODEL_CHECKPOINT=datalab-to/surya-ocr-2) for geometry. Github : datalab-to/surya huggingface.co
You can find Surya here: Model - huggingface.co Code - datalab-to/surya Blog - datalab.to
@VikParuchuri how do compare with SuryaOCR datalab-to/surya