Datasets:
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pretty_name: "Chinese OCR Line Dataset (EvaHan Competition @ LREC2026)"
task_categories:
- image-to-text
language:
- zh
license: cc-by-4.0
tags:
- ocr
- historical-documents
- chinese
- lrec-2026
links:
paper: "https://hal.science/hal-05622586"
repository: "https://github.com/<org>/<repo>"
competition: "https://<competition-url>"
---
# Chinese OCR Line Dataset
This repository contains the synthetic dataset used by the ENCHANTeam team for the EvaHan 2026 competition on OCR/HTR of Chinese documents.
**Final ranking of the team**: 3rd.
UltraGlyph data consist in a automatic mix of real data provided by the organizers, for the closed modality of the competition (no external dataset authorized).
| Resource | Link |
|----------|------|
| Preprint | [HAL](https://hal.science/<HAL-ID>) |
| Code | [GitHub](https://github.com/Bizais-Lillig/enchanteam) |
| Competition | [EvaHan @ LREC 2026](https://circse.github.io/LT4HALA/2026/EvaHan) |
## Dataset description
Pairs of line images and Chinese transcriptions drawn from two sources:
| Source label | Origin | Train | Val |
|---|---|---|---|
| `fonts` | Synthetic printed lines using chinese fonts | 72 323 | 12 676 |
| `ultraglyph` | Synthetic lines using real data and ultraglyph generator (mix handwritten and printed) | 57 063 | 10 201 |
| **Total** | | **129 386** | **22 877** |
Transcription lengths range from 2 to 30 characters (median 16).
## Usage
```python
from datasets import load_dataset
ds = load_dataset("<org>/<dataset-name>")
example = ds["train"][0]
example["image"].show()
print(example["transcription"], example["source"])
```
## Citation
If you use this dataset, please cite this work:
```bibtex
@misc{<bibtex-key>,
title = {},
author = {},
year = {2026},
url = {https://hal.science/<HAL-ID>}
}
```
## Acknowledgement
This study was conducted as part of the DH master’s program at École nationale des chartes–PSL and with the support of the PSL Research University’s Major Research Program CultureLab, implemented by the ANR (reference ANR-10-IDEX-0001). |