---
license: apache-2.0
base_model: lightonai/LightOnOCR-3-4B
library_name: transformers
pipeline_tag: image-to-text
tags:
- ocr
- table-recognition
- document-understanding
datasets:
- lschiemanowski/scannedtables
---

# tablereader-lightonocr-v0

[LightOnOCR-3-4B](https://huggingface.co/lightonai/LightOnOCR-3-4B) fine-tuned to transcribe a crop of a printed
table into a fixed HTML format: a `# Title` line when a title is printed, a table with `thead` for printed
column headings, `data-level` for the printed indentation of row labels, `<sup>` for footnote markers and
`tfoot` for footnote lines ([transcription format](https://github.com/lschiemanowski/tablereader/blob/main/docs/transcription-format.md),
contract revision 1).

It is the model behind [tablereader](https://github.com/lschiemanowski/tablereader), which detects the tables
in a PDF, crops them over their titles and footnotes, runs this model, and converts the transcription into
table JSON. `release.json` records the settings the model was trained with: crop preparation, renderer,
detector, image size and generation settings. Use it through tablereader so crops are prepared the same way:

```bash
uv run tablereader read report.pdf --pages 13-20
```

## Prompt

The image alone, with no system prompt or text, scaled so its longest edge is at most 2048 pixels; thinking
disabled, temperature 0, up to 16,000 new tokens.

## Training

LoRA (rank 16, alpha 32) on every language-model projection, the vision encoder frozen; 3 epochs at learning
rate 1e-4 on 995 table crops from 107 publication series (UK public-sector reports, US federal and historical
statistics, German budget annexes and others), digital and scanned. The targets are GPT-6.1 Terra
transcriptions that parse into tables without blocking issues; they were not reviewed by a person. The training
crops include some that the [scannedtables](https://huggingface.co/datasets/lschiemanowski/scannedtables)
dataset holds back for rights or privacy reasons. The adapter is merged into the bf16 weights.

## Evaluation

1,006 validation crops from series not seen in training, scored as the share of printed values whose row path,
column path and text match the reference transcription exactly (metric `records-1`):

| All | Digital PDFs | Scans |
| ---: | ---: | ---: |
| 0.797 | 0.835 | 0.511 |

72% of crops are read without a difference. The model does not yet answer `NOT_A_TABLE` for crops without a
table, and scans read much worse than digital PDFs. A self-training round on 7,698 of its own
transcriptions did not improve these scores.

## License

Apache 2.0, as the base model.