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surya-english-tables-part2

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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_021.png
<table border="1"> <thead> <tr> <th rowspan="2">Ministry/Agency</th> <th colspan="4">Total Planned Expenditure</th> <th colspan="4">Committed Funds</th> <th colspan="4">Additional Funds Required</th> </tr> <tr> <th>2015</th> <th>2016</th> <th>2017</th> <th>Total<br/>2015-2017</th> <th>Source of<br/>Funds</th> <th>2015<...
[{"rows": [{"polygon": [[3.22, 2.043], [3216.78, 2.043], [3216.78, 55.161], [3.22, 55.161]], "confidence": null, "row_id": 0, "bbox": [3.22, 2.043, 3216.78, 55.161]}, {"polygon": [[3.22, 59.247], [3216.78, 59.247], [3216.78, 145.053], [3.22, 145.053]], "confidence": null, "row_id": 1, "bbox": [3.22, 59.247, 3216.78, 14...
[{"model": "datalab-to/surya-ocr-2", "model_name": "surya-ocr-2", "column_name": "markdown", "blocks_column": "surya_blocks", "task": "table", "table_mode": "full", "backend": "vllm-offline", "page_range": null, "error_rate": 0.0, "timestamp": "2026-08-07T04:38:07.483992+00:00", "script": "surya-ocr.py"}]
PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_022.png
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_023.png
"<table border=\"1\">\n<thead>\n<tr>\n<th rowspan=\"2\">Ministry/Agency</th>\n<th colspan=\"4\">Tota(...TRUNCATED)
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_024.png
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_025.png
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_026.png
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_027.png
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"[{\"rows\": [{\"polygon\": [[3.221, 2.115], [3217.779, 2.115], [3217.779, 54.989999999999995], [3.2(...TRUNCATED)
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_028.png
"<table border=\"1\">\n<thead>\n<tr>\n<th rowspan=\"2\">Ministry/Agency</th>\n<th colspan=\"4\">Tota(...TRUNCATED)
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_029.png
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PublicInvestmentProgram3YearRolling,FY2015-FY2017_page_030.png
"<table border=\"1\">\n<thead>\n<tr>\n<th rowspan=\"3\">NSDP 2014-2018<br/>Sub-sector</th>\n<th>NSDP(...TRUNCATED)
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"[{\"model\": \"datalab-to/surya-ocr-2\", \"model_name\": \"surya-ocr-2\", \"column_name\": \"markdo(...TRUNCATED)
End of preview. Expand in Data Studio

Surya OCR 2 (table) on sopheakvoatei/english-table-dataset-part2

Table recognition (mode full) over images in sopheakvoatei/english-table-dataset-part2 using Surya OCR 2 (650M, Qwen3.5-based) by Datalab, via the surya-ocr package, run as offline vLLM batch inference on Hugging Face Jobs.

Processing Details

  • Source Dataset: sopheakvoatei/english-table-dataset-part2
  • Model: datalab-to/surya-ocr-2
  • Task: table (table mode full)
  • Input column: image (image)
  • Text column: markdown (flattened, reading-order text per row)
  • Structured column: surya_blocks (JSON: per-page blocks with bbox / polygon / label / reading_order / confidence / html)
  • Split: train
  • Samples: 322
  • Processed OK: 322 / 322
  • Processing time: 20.3 min
  • Date: 2026-08-07 04:38 UTC

License note

Surya's code is Apache-2.0, but the model weights use a modified OpenRAIL-M license: free for research, personal use, and startups under $5M funding/revenue, restricted from competitive use against Datalab's API. See the model card.

Dataset Structure

Original columns plus:

  • markdown: flattened text (OCR), label outline (layout), or table HTML (table)
  • surya_blocks: structured result as a JSON string (one entry per page)
  • inference_info: JSON list tracking models applied to this dataset

Generated with UV Scripts.
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