Dataset Viewer
Auto-converted to Parquet Duplicate
image
imagewidth (px)
2.64k
3.71k
file_name
stringclasses
3 values
markdown
stringclasses
3 values
surya_blocks
stringclasses
3 values
inference_info
stringclasses
1 value
PublicInvestmentProgram3YearRolling,FY2013-FY2015_page_010.png
<table> <tbody><tr> <th>αžŸαžΌαž…αž“αžΆαž€αžšαžŸαŸαžŠαŸ’αž‹αž€αž·αž…αŸ’αž…</th> <th>្០៑្ αž‚αžΊ</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑៣ αž‚αžΊ</th> <th>αž†αŸ’αž“αžΆαŸ†αŸ’αŸ αŸ‘αŸ€ αž‚αžΊ</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑αŸ₯ αž‚αžΊ</th></tr> <tr> <td>αž•αž›αž·αžαž•αž›αž€αŸ’αž“αž»αž„αžŸαŸ’αžšαž»αž€αžŸαžšαž»αž” (GDP) αž‚αž·αžαžαŸ’αžšαžΉαž˜αžαž˜αŸ’αž›αŸƒαž”αž…αŸ’αž…αž»αž”αŸ’αž”αž“αŸ’αž“ (αž–αžΆαž“αŸ‹αž›αžΆαž“αžšαŸ€αž›)</td> <td>αŸ₯៨,៑៩្</td> <td>៦ៀ,០៨្</td> <td>៧០,៦៧៧</td> <td>៧៧,ៀ៧៩</td></tr> <tr> <td>αž•αž›αž·αžαž•αž›αž€αŸ’αž“αž»αž„αžŸαŸ’αžšαž»αž€αžŸαžšαž»αž” (GDP)...
[{"rows": [], "cols": [], "cells": [], "image_bbox": [0.0, 0.0, 3712.0, 1680.0], "raw": "<table>\n<tbody><tr>\n<th>αžŸαžΌαž…αž“αžΆαž€αžšαžŸαŸαžŠαŸ’αž‹αž€αž·αž…αŸ’αž…</th>\n<th>្០៑្ αž‚αžΊ</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑៣ αž‚αžΊ</th>\n<th>αž†αŸ’αž“αžΆαŸ†αŸ’αŸ αŸ‘αŸ€ αž‚αžΊ</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑αŸ₯ αž‚αžΊ</th></tr>\n<tr>\n<td>αž•αž›αž·αžαž•αž›αž€αŸ’αž“αž»αž„αžŸαŸ’αžšαž»αž€αžŸαžšαž»αž” (GDP) αž‚αž·αžαžαŸ’αžšαžΉαž˜αžαž˜αŸ’αž›αŸƒαž”αž…αŸ’αž…αž»αž”αŸ’αž”αž“αŸ’αž“ (αž–αžΆαž“αŸ‹αž›αžΆαž“αžšαŸ€αž›)</td>\n<td>αŸ₯៨...
[{"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": "2026-08-19T02:58:31.990663+00:00", "script": "surya-ocr.py"}]
PublicInvestmentProgram3YearRolling,FY2013-FY2015_page_011.png
<table> <tbody><tr> <th></th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑្</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑៣</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑ៀ</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑αŸ₯</th></tr> <tr> <td>αž€αžŸαž·αž€αž˜αŸ’αž˜ αž“αŸαžŸαžΆαž‘ αž“αž·αž„αž–αŸ’αžšαŸƒαžˆαžΎ</td> <td>៑,៨%</td> <td>ៀ.្%</td> <td>៣.្%</td> <td>៣.៑%</td></tr> <tr> <td>αžŠαŸ†αžŽαžΆαŸ†</td> <td>៑,៨%</td> <td>αŸ₯.្%</td> <td>៣.៦%</td> <td>៣.៣%</td></tr> <tr> <td>αžŸαžαŸ’αžœαž…αž·αž‰αŸ’αž…αžΉαž˜ αž“αž·αž„αž”...
[{"rows": [], "cols": [], "cells": [], "image_bbox": [0.0, 0.0, 2640.0, 4242.0], "raw": "<table>\n<tbody><tr>\n<th></th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑្</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑៣</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑ៀ</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑αŸ₯</th></tr>\n<tr>\n<td>αž€αžŸαž·αž€αž˜αŸ’αž˜ αž“αŸαžŸαžΆαž‘ αž“αž·αž„αž–αŸ’αžšαŸƒαžˆαžΎ</td>\n<td>៑,៨%</td>\n<td>ៀ.្%</td>\n<td>៣.្%</td>\n<td>៣.៑%</td></tr>\n<tr>...
[{"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": "2026-08-19T02:58:31.990663+00:00", "script": "surya-ocr.py"}]
PublicInvestmentProgram3YearRolling,FY2013-FY2015_page_012.png
<table> <tbody><tr> <th>αž”αŸ’αžšαž—αž–αž αž·αžšαž‰αŸ’αž‰αž”αŸ’αž”αž‘αžΆαž“</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑្</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑៣</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑ៀ</th> <th>αž†αŸ’αž“αžΆαŸ† ្០៑αŸ₯</th> <th>αžŸαžšαž»αž” αž†αŸ’αž“αžΆαŸ†αŸ’αŸ αŸ‘αŸ£ -្០៑αŸ₯</th> </tr> <tr> <td>αž€αžΆαžšαžœαž·αž“αž·αž™αŸ„αž‚αžŸαžšαž»αž”</td> <td>៣,ៀ៣ៀ.៧</td> <td>៣,៨៩αŸ₯.ៀ</td> <td>ៀ,៑៧្.០</td> <td>ៀ,αŸ₯៩៧.៧</td> <td>៑្,៦៦៦.៑</td> </tr> <tr> <td>Β· αž€αžΆαžšαžœαž·αž“αž·αž™αŸ„αž‚αžŸαžΆαž’αžΆαžšαžŽαŸˆ</td> <t...
[{"rows": [], "cols": [], "cells": [], "image_bbox": [0.0, 0.0, 2768.0, 2584.0], "raw": "<table>\n<tbody><tr>\n<th>αž”αŸ’αžšαž—αž–αž αž·αžšαž‰αŸ’αž‰αž”αŸ’αž”αž‘αžΆαž“</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑្</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑៣</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑ៀ</th>\n<th>αž†αŸ’αž“αžΆαŸ† ្០៑αŸ₯</th>\n<th>αžŸαžšαž»αž” αž†αŸ’αž“αžΆαŸ†αŸ’αŸ αŸ‘αŸ£ -្០៑αŸ₯</th>\n</tr>\n<tr>\n<td>αž€αžΆαžšαžœαž·αž“αž·αž™αŸ„αž‚αžŸαžšαž»αž”</td>\n<td>៣,ៀ៣ៀ.៧</td>\n<td>៣,៨៩αŸ₯....
[{"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": "2026-08-19T02:58:31.990663+00:00", "script": "surya-ocr.py"}]

Surya OCR 2 (table) on sopheakvoatei/rendered_khmer_tables

Table recognition (mode full) over images in sopheakvoatei/rendered_khmer_tables 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/rendered_khmer_tables
  • 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: 3
  • Processed OK: 3 / 3
  • Processing time: 3.0 min
  • Date: 2026-08-19 02:58 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.

Downloads last month
6