Datasets:
image imagewidth (px) 36 5.7k | text stringlengths 1 261 |
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α αααΈαααΌα αα·α ααα α’ααα αααααΎαα‘αΎα | |
αααααΆαααααΆαααΈαααααΆαα ααααΆααααΌααα
αα
αααα·ααααΆ | |
α α
αααααααααααΆαα)ααααΆααααααΆα | |
αααα»ααααααα αα·αααΎααΆαα’ααααααΆαα· | |
αααααα - DAP Newsα§ααααααααΈααα―α ααα | |
α αΎαααΆαααααΎααΈααΉαααΎαααΈ | |
αα»ααΈ αα½α ααα ααΈααα α― ααΆα | |
"Some may also be noted in individuals with other types of deficits or disorders, such as attention deficits, hearing loss, behavioral problems, and learning difficulties or dyslexia." | |
αααααα»ααααααΆααααΈα
α·ααααααααfreshnewsasia. | |
αααααΆα αααα αα α£α€α ααΈα‘αΌαααααα ( α’α‘α‘ | |
αααααΆαααααα²ααααΆαααααααααΎα | |
ααααα»α αααα»α ααααΆ ααα ααΎααααΈα²αα | |
ααΌαααααΆαααΌα
ααΆαααΌααα Chiron α αΎααααααααα | |
ααααααΆαα½ααααααα 50 ααααα | |
α ααα αα½α ααα ααΆα ααα
ααααΈααα | |
Fritters in general is a very common stuff in Indian cooking. | |
ααααααα
αα·αααΆααΆααΌα‘ ααααΎααααΎααα
ααΎ | |
α ααΆαααΎα‘αΎααα
αααα»ααα½ααα·ααΆααααΆααΆαααΆα | |
αα
αααα»α ααααΆα αααΉα αα½α | |
ααΆααα‘α»α (Thang Long) ααΆααααΆαα‘α α‘α α | |
αα·α αααααΆαα αα»ααααα αα½ααα ααΆααα’αα ααα | |
bogeymen | |
We look at how Somalia's drought is taking a brutal toll on the youngest victims. | |
αα»αααααΆααααααΎααααΆαααααα αα·ααααα | |
ααααΆαααΆαααΎααααααΎααααααΆααααααααΉααα | |
αααααααΆααααααα ααΌα ααααααα | |
ααΆ α€ ααα ααΆααΆαα
α αα»αα | |
ααΆα α
αΌα α―α αα
| |
ααα ααΈα’ αα»α α.α ααΊ αα
αααα»α | |
ααααΌαααΆααααααα
α·αααααΉαααααααααααααα | |
ααΆαα ααΆ ααα αα»α | |
αααα
αααα»αααΈαααααααα·αα·ααααα½α | |
α±ααααΆααααα ααααΆαααα·ααααΆααααα·ααααΆααΆααααααα»ααΆαα·ααΆα | |
αα»ααααααααααΎααΆα₯ααα ααΆαα
αΌα | |
αααααααααααααα»ααα½α | |
αααα»αααα’αΆα ααΆαααααΎααααα
ααααΆα | |
P<SGPCHAN<<SAMNITH<<SOKHOM<<<<<<<<<<<<<<<<<< | |
ααααΆ α’ααααΎαα
αΌααα½αααΆα’αα·αααΈα’αα’αααΆαα | |
ααα’αααΆααΆααααΌαααααααααααΆαααΆα | |
He rallied against the Vietnam War. | |
[ααΉα-α-ααΆα] | |
α
αααΎα αααααααΆααΈααΆαααααΎααααΆααααααα’αα·αα·αα | |
αααα»α α α’ααααααα»α ααα αα
| |
ααααα ααααα·α ααΆαα (vassika) | |
squeezably | |
αα
αα
α»αααααααα
ααΌαα·ααΌαααα»α αα»αααααΆ | |
αα·ααα ααααα’αΆαααα·α αααααΆαααααα | |
unmortising | |
ααα α’αΆααΆαααΆαα»αα
ααααααα½ααααα | |
ααΎαααΈααααα
ααα α’ααα | |
"""athletes only, the smallest delegation since""" | |
"""αααααα, MNααΎααααΈααΆααααααα""" | |
αα
ααααααααΆαααααααααααΆαααΈαααΆαααααααΆα | |
ααΎαααα αα»ααααα·ααΆα | |
ααααααααααΆα ααΉαααα½αααΆαααΈααααΆαααΆαααα | |
ααΆααααα»ααααααααααααααααα»αααααΌααααααααα»αααα | |
ααΆαααααααααααα ααΎα | |
ααααααΈ α’ ααα αα»ααα ααα·ααα αα·αα | |
αα»α ααα ααΆααα·α
α αα
| |
"""α
αααΆα
ααα·ααααααα, ααΆαα""" | |
αααααααΈααΆαααΆα αα·αα’ααΈααα»αααααααΆααααααα | |
ααααααααααααααααααα
αααααααα | |
αα·αααΌαααα»ααααΆαααααα αααααα | |
αα½αααΈ αα
ααααααααΆαα₯αααα | |
αααααααααααΈα αα» αααα»ααααααααααΉαααα
ααααΈαα·ααΆα | |
α’α’αΆ αα·αα©α’αΆ αααα»α α€ ααΌαα· | |
αααα αααααααααΌαααΆααααααααΈααααΎα | |
"""αααααααΉα""""αααα’αααααΉαααα½αααΆααααααα""" | |
ααΆαα’αα·ααααααΈα‘αΆααΆααααΆαα αα»ααα αα·αααα·ααααΆα | |
ever-recurrent | |
ααΎα ααΆα ααΆαααααααααα·α ααααΆ | |
α
α·αααΆααααααααααααΆαα’ααααααΆαα·α
ααααααα | |
ααΆ ααΏα ααααΆαα αα | |
ααααααΆαααΆααα·ααΆαααΆαα»ααααααΎα | |
ααΎαααα
αααααα
α’α α‘α ααα.ααΎ | |
ααΆ ααΆααΆ ααΆααΆαα αα | |
α αΎα ααΆαααααΆααααααΆαααΈααα | |
αα½αααααααΌαααΆαα
αΆαααα»αααΆααΆααααα | |
waddles | |
αααα ααΆαααΆαααααααΎαααααΎααΆα | |
αααα
αααα·ααααααΆααααααααααααααααΉα | |
ααααΆαα’αα·ααααααααααα·α
αα
ααααααααααααααα | |
ααΌα ααΆαα αα
αααα»α αα α₯ααΈ ααΎα | |
αααααααα ααΆααααααααααΆαααΆααααααααα | |
αα
αααααΆααααααΆαααΆαααααΆαααααΆα | |
αααααααΆααΈααααΉααααΆααααα·αα»αααΆαα·αααα»α | |
αααα»α ααααΌα ααα ααα ααα αααα | |
αααα»ααααααΆαααααΈα αααααα§ααααααΆααααααΆα | |
ααΎααααα
αααααααααααα·ααα
αααα»α | |
ααΆαα ααΆαα ααΆαα ααΈ | |
itself had set a bold new | |
ααΆαααΆααααααΆαααααΆ ααΆααααΎαααα | |
αααααΆααααααααα·αααααααααααααΌαααΆαααααα | |
ααα»αααααααΆααΎαααΆαα αααααΆα | |
ααα ααααΆα
αα’αα ααα | |
α
α·ααα ααααααααααααα’α ααΆαααα | |
αα α
ααα‘αΆ ααΉα αα·α
| |
ααΆαααααααααΆααααααααααααα±αααα
| |
α‘αΎαααΈαααααα
ααα½αα€ ααααΎα²αα | |
ααΆαα
αΌααα½ααααΆααα»ααα»α ααΈααααΆααα±ααααΆααΈ |
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Dataset Card for Khmer English OCR 200K
Dataset Summary
Khmer-English OCR 200K is a line-level OCR dataset stored as parquet files with two columns:
image: a struct containing raw image bytes and apathfieldtext: the transcription string for the image
The dataset currently contains:
train: 179,988 examplesval: 19,996 examples
This dataset appears to target OCR training for Khmer and mixed Khmer-English text.
Dataset Structure
Data Instances
Each row has the following structure:
{
"image": {
"bytes": b"...",
"path": "train_000256.png",
},
"text": "α αααΈαααΌα αα·α ααα α’ααα αααααΎαα‘αΎα",
}
Data Fields
image.bytes: Raw bytes of the image file.image.path: Original image filename fromlabels.txt(for exampletrain_000256.png).text: UTF-8 transcription text. During parquet creation, carriage returns and newlines are removed.
Intended Uses
- Training OCR models for mixed Khmer-English line recognition
- Validation and benchmarking for sequence recognition models
Limitations
- The dataset may contain noisy labels, mixed scripts, punctuation variation, or OCR-unfriendly crops.
- The transcription preprocessing removes newline and carriage return characters.
Citation
If you publish or share this dataset, add a citation here:
@dataset{kh_en_ocr_200k,
title = {Khmer Image to Text dataset 200K images},
author = {LazyGreed},
year = {2026},
note = {Line-level OCR dataset}
}
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