Datasets:
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license: mit
language:
- km
task_categories:
- image-to-text
tags:
- ocr
- khmer-ocr
- benchmark
pretty_name: KHOB (325 lines)
size_categories:
- n<1K
---
# KHOB, 325 text lines
325 Khmer text-line images with their transcriptions, cropped from the [Khmer OCR Benchmark (KHOB)](https://github.com/EKYCSolutions/khmer-ocr-benchmark-dataset) by EKYC Solutions: scanned Cambodian government documents with clean backgrounds and compression artefacts. One image is one line.
This is the KHOB test set in the [Netra OCR](https://github.com/netra-ai-lab/Netra-OCR) results, where the recognizer scores 0.96% CER with beam search and 1.29% greedy.
| Column | |
| --- | --- |
| `image` | the line crop |
| `label` | its text |
| `file_name` | the original crop's name: task, document and line number |
## Use
```python
from datasets import load_dataset
ds = load_dataset("Netra-Lab/khob-eval", split="train")
```
With the Netra OCR repository, the `khob` preset scores any model on it:
```bash
bash training/evaluate.sh published --test khob
```
## License
MIT, as the original KHOB dataset. Credit: EKYC Solutions, [khmer-ocr-benchmark-dataset](https://github.com/EKYCSolutions/khmer-ocr-benchmark-dataset).
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