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αž αŸ’αž‚αžΈαžšαŸ‰αžΌαžŠ αž“αž·αž„ αž–αŸαž‡ αž’αž„αŸ’αž‚ αžŠαŸ‚αž›αž›αžΎαž€αž‘αžΎαž„
αž€αž˜αŸ’αž›αžΆαŸ†αž„αž˜αŸ‰αžΆαžŸαŸŠαžΈαž“αžαŸ’αž›αžΆαŸ†αž„ αž—αŸ’αž‡αžΆαž”αŸ‹αž“αžΌαžœαž”αž…αŸ’αž…αŸαž€αžœαž·αž‘αŸ’αž™αžΆ
αž αŸ…αžœαžαŸ’αžαž–αŸ’αžšαŸ‚αž€αžšαžΆαŸ†αž„)αžšαž„αž€αžΆαžšαž‚αž˜αŸ’αžšαžΆαž˜
αž€αŸ’αž“αž»αž„αž”αŸ’αžšαž‘αŸαžŸ αž“αž·αž„αž›αžΎαž†αžΆαž€αž’αž“αŸ’αžαžšαž‡αžΆαžαž·
αžšαžαž™αž“αŸ’αž - DAP Newsαž§αžαŸ’αžαž˜αžŸαŸαž“αžΈαž™αŸαž―αž€ αžŸαŸ„αž˜
αž αžΎαž™αž€αžΆαžšαž”αŸ’αžšαžΎαž‡αžΈαž‘αžΉαž€αž›αžΎαž€αž‘αžΈ
αžŸαž»αž—αžΈ αž‡αž½αž” αž™αŸ‰αž αžŒαžΈαžŸαŸ‚αž› ឯ មអស
"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."
αž‚αŸαž”αŸ†αž•αž»αžαž”αž“αŸ’αž‘αžΆαž”αŸ‹αž–αžΈαž…αž·αž“αŸ•αž”αŸ’αžšαž—αž–αŸ–freshnewsasia.
αžœαŸ€αžαžŽαžΆαž˜ αž”αŸ’αžšαž αŸ‚αž› ៣ៀ០ αž‚αžΈαž‘αžΌαž˜αŸ‰αŸ‚αžαŸ’αžš ( ្៑៑
αžαŸ‚αžšαž‘αžΆαŸ†αž†αŸ’αž˜αž”αž²αŸ’αž™αž€αžΆαž“αŸ‹αžαŸ‚αž”αŸ’αžšαžŸαžΎαžš
αž”αŸ’αžšαž‡αž»αŸ† αž€αŸ’αž“αž»αž„ αž‚αŸ’αžšαžΆ αž“αŸαŸ‡ αžŠαžΎαž˜αŸ’αž”αžΈαž²αŸ’αž™
αž˜αžΌαž›αžŠαŸ’αž‹αžΆαž“αžŠαžΌαž…αž‡αžΆαž˜αŸ‰αžΌαžŠαŸ‚αž› Chiron αž αžΎαž™αž€αŸαžŸαŸ’αžšαžŠαŸ€αž„
αžšαžŠαŸ’αž‹αžŽαžΆαž˜αž½αž™αž“αŸƒαžšαžŠαŸ’αž‹ 50 αž•αŸ’αžαž›αŸ‹
αŸ” αž“αŸαž™ αž˜αž½αž™ αž‘αŸ€αž តអម αžŸαŸαž…αž€αŸ’αžαžΈαž™αž›αŸ‹
Fritters in general is a very common stuff in Indian cooking.
αž–αžŽαŸŒαžαŸ’αž˜αŸ… αž“αž·αž„αžŸαžΆαž€αžΆαžŠαžΌαŸ‘ αž’αŸ’αžœαžΎαžŠαŸ†αžŽαžΎαžšαž“αŸ…αž›αžΎ
αŸ” αž€αžΆαžšαž›αžΎαž‘αžΎαž„αž“αŸ…αž€αŸ’αž“αž»αž„αž‡αž½αž”αž–αž·αž—αžΆαž€αŸ’αžŸαžΆαž€αžΆαžšαž„αžΆαžš
αž“αŸ…αž€αŸ’αž“αž»αž„ αžŸαŸ’αž›αžΆαž™ αž„αž„αžΉαž αž˜αž½αž™
αžαžΆαŸ†αž„αž‘αž»αž„ (Thang Long) αž“αžΆαž†αŸ’αž“αžΆαŸ†αŸ‘αŸ αŸ‘αŸ αŸ”
αž˜αž·αž“ αžαŸ‚αž˜αž‘αžΆαŸ†αž„ αž”αž»αžŽαŸ’αžŽαŸ„αŸ‡ αž–αž½αž€αž‚αŸ αž‘αžΆαŸ†αž„αž’αžŸαŸ‹ αž“αŸ„αŸ‡
bogeymen
We look at how Somalia's drought is taking a brutal toll on the youngest victims.
αž€αž»αŸ†αžŸαŸ’αž‘αžΆαž€αŸ‹αžŸαŸ’αž‘αžΎαžšαž‰αŸ‰αžΆαŸ†αž”αž“αŸ’αž›αŸ‚ αž“αž·αž„αž•αŸ’αž›αŸ‚
αž†αŸ’αž“αžΆαŸ†αž€αžΆαžšαž”αžΎαž”αž‘αž›αŸ’αž˜αžΎαžŸαž“αŸαŸ‡αž”αžΆαž“αž”αŸ’αžšαž–αŸ’αžšαžΉαžαŸ’αž
αžŸαž€αž˜αŸ’αž˜αž—αžΆαž–αžŸαž„αŸ‹αžšαŸ„αž„ αžαžΌαž” αž”αŸ‚αžšαž”αž“αŸαŸ‡
αž‘αžΆ ៀ αžŠαŸ„αž™ αž€αžΆαž˜αžΆαžœαž…αžš αž€αž»αžŸαž›
αžαžΆαŸ– αž…αžΌαžš αž―αž„ αž‘αŸ…
αžŠαž›αŸ‹ αž‘αžΈαŸ’ αž˜αž»αž“ αž‚.ស αž‚αžΊ αž“αŸ…αž€αŸ’αž“αž»αž„
αžαŸ’αžšαžΌαžœαž”αžΆαž“αž‚αŸαž‡αž€αŸ‹αž…αž·αžαŸ’αžαž“αžΉαž„αž‘αŸαž–αž€αŸ„αžŸαž›αŸ’αž™αžšαž”αžŸαŸ‹
αžŠαžΆαž€αŸ‹ αž‡αžΆ αž‘αŸαž– មុខ
αžαžαž“αŸ…αž€αŸ’αž“αž»αž„αž‘αžΈαž€αž“αŸ’αž›αŸ‚αž„αž–αž·αžŸαž·αžŠαŸ’αž‹αž˜αž½αž™
αž±αžŸαžαžŸαžΆαžŸαŸ’αžαŸ’αžš αž“αŸƒαžŸαžΆαž€αž›αžœαž·αž‘αŸ’αž™αžΆαž›αŸαž™αžœαž·αž‘αŸ’αž™αžΆαžŸαžΆαžŸαŸ’αžαŸ’αžšαžŸαž»αžαžΆαž—αž·αž”αžΆαž›
αžŸαž»αžαŸ’αžαž“αŸ’αžαž”αŸ’αžšαžΎαž‡αžΆαž₯αž“αŸ’αž‘ αž”αžΆαž“αž…αžΌαž›
αžŠαžŠαŸ‚αž›αŸ—αž”αž€αžŸαŸ’αž”αŸ‚αž€αž˜αž»αžαž–αž½αž€
αžŸαŸ’αžšαž»αž€αžŸαŸ’αž’αžΆαž„ αž”αžΆαž“αžšαž€αžƒαžΎαž‰αž˜αŸ’αžŸαŸ…αž€αŸ’αžšαžΆαž˜
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αžŸαŸαž“αžΆ αž’αž‰αŸ’αž‡αžΎαž‰αž…αžΌαž›αžšαž½αž˜αž‡αžΆαž’αž’αž·αž”αžαžΈαž’αž”αž’αžšαžŸαžΆαž‘αžš
αžšαž„αž’αž˜αžŸαžΆαž›αžΆαžŠαŸ†αž”αžΌαž„αžαŸαžαŸ’αžαž€αžŽαŸ’αžαžΆαž›αž”αžΆαž“
He rallied against the Vietnam War.
[រឹស-រ-αž™αžΆαž„]
αž…αŸ’αžšαžΎαž“ αž€αžαŸ‹αžαŸ’αžšαžΆαž–αžΈαž€αžΆαžšαž”αŸ’αžšαžΎαž”αŸ’αžšαžΆαžŸαŸ‹αžšαž”αžŸαŸ‹αž’αžαž·αžαž·αž‡αž“
αžŸαŸ†αž”αž»αž€ αŸ” αž’αŸ’αž“αž€αžŸαŸ’αžšαž»αž€ αžŠαŸ‚αž› αž‘αŸ…
αžŸαŸ†αž–αžαŸ‹ αžœαžŸαŸ’αžŸαž·αž€ αžŸαžΆαžŠαž€ (vassika)
squeezably
αž”αž…αŸ’αž…αž»αž”αŸ’αž”αž“αŸ’αž“αž“αŸ…αž—αžΌαž˜αž·αž–αžΌαžαŸ’αžšαž»αŸ† αžƒαž»αŸ†αžšαž˜αž“αžΆ
αž“αž·αž„αžŸαž αžšαžŠαŸ’αž‹αž’αžΆαž˜αŸαžšαž·αž€ αžŠαŸ‚αž›αž”αžΆαž“αž‚αŸ’αžšαŸ„αž„
unmortising
αž“αŸαŸ‡ αž’αžΆαž€αžΆαžŸαž’αžΆαžαž»αž“αŸ… αžαŸ†αž”αž“αŸ‹αž‡αž½αžšαž—αŸ’αž“αŸ†
αž›αžΎαžŸαž–αžΈαž“αŸαŸ‡αž‘αŸ…αž‘αŸ€αž αž’αž„αŸ’αž‚
"""athletes only, the smallest delegation since"""
"""αžαŸ’αž˜αž—αž€αŸ‹, MNαžŠαžΎαž˜αŸ’αž”αžΈαž‚αžΆαŸ†αž‘αŸ’αžšαžŠαž›αŸ‹"""
αž‘αŸ…αžŠαž›αŸ‹αž”αŸ’αžšαž‡αžΆαž–αž›αžšαžŠαŸ’αž‹αžŠαŸ‚αž›αž˜αžΆαž“αž‡αžΈαžœαž—αžΆαž–αžαŸ’αžœαŸ‡αžαžΆαž
αžƒαžΎαž‰αž˜αž€αŸ” αž€αž»αŸ†αž˜αž€αž“αž·αž™αžΆαž™
αžαŸαžαŸ’αžαžŸαŸ€αž˜αžšαžΆαž” αž“αžΉαž„αž‘αž‘αž½αž›αž”αžΆαž“αž‘αžΈαž•αŸ’αžŸαžΆαžšαž€αžΆαž“αŸ‹αžαŸ‚
αž€αžΆαžšαž”αŸ’αžšαž»αž„αž”αŸ’αžšαž™αŸαžαŸ’αž“αžšαž”αžŸαŸ‹αž€αŸ’αžšαž»αž˜αž‚αŸ’αžšαžΌαž–αŸαž‘αŸ’αž™αž€αŸ’αž“αž»αž„αžœαŸαž“
αžœαžΆαž€αŸαžŠαŸαž€αž›αž€αŸ‹αž˜αŸ‚αž“αŸ” αžƒαžΎαž‰
αžαŸ’αž„αŸƒαž‘αžΈ ្ αž“αŸαŸ‡ αž–αž»αž‘αŸ’αž’ αž”αžšαž·αžŸαŸαž‘ αž“αž·αž™αž˜
មុខ αž›αŸ„αž€ αžαžΆαž„αž›αž·αž… αŸ” αž“αŸ…
"""αž…αž„αž”αžΆαž…αŸ‹αžœαž·αž›αž˜αž€αž•αŸ’αž‘αŸ‡, αžŠαžΆαž€αŸ‹"""
αž˜αž“αŸ’αžαŸ’αžšαžΈαžšαžΆαž‡αž€αžΆαžš αž“αž·αž„αž’αžαžΈαžαž™αž»αž‘αŸ’αž’αž‡αž“αž˜αžΆαž“αž›αž€αŸ’αžαžŽαŸˆ
αž”αŸ’αžšαž‚αŸαž“αžŠαž›αŸ‹αž–αŸ’αžšαŸ‡αžŸαž„αŸ’αžƒαž“αŸ…αžœαžαŸ’αžαž–αŸ’αžšαŸƒ
αž˜αž·αž“αž™αžΌαžšαž”αŸ‰αž»αž“αŸ’αž˜αžΆαž“αž‘αŸ€αžαž‘αŸ αž”αŸ’αžšαž‘αŸαžŸ
αžαž½αž€αž‚αžΈ αž“αŸ…αžαŸ‚αž”αž“αŸ’αžαž˜αžΆαž“αž₯αžαžˆαž”αŸ‹
αžαŸαžαŸ’αžαž–αŸ’αžšαŸ‡αžŸαžΈαž αž“αž» αž€αŸ†αž–αž»αž„αžαŸ‚αž”αŸ’αžšαžˆαž˜αž“αžΉαž„αžŸαŸαž…αž€αŸ’αžαžΈαžœαž·αž“αžΆαžŸ
្ធអ αž“αž·αž„αŸ©αž’αžΆ αž€αŸ’αžšαž»αž˜ ៀ αž—αžΌαž˜αž·
αž”αŸ’αžšαž αŸ„αž„αžαžαž”αŸαŸ‡αžŠαžΌαž„αžαžΆαž„αž€αŸ’αžšαŸ„αž˜αž–αžΈαž€αŸ†αžŽαžΎαž
"""αž–αŸαž›αž–αŸ’αžšαžΉαž€""""αž“αŸ„αŸ‡αž’αŸ’αž“αž€αž“αžΉαž„αž‘αž‘αž½αž›αž”αžΆαž“αž›αž‘αŸ’αž’αž•αž›"""
αž€αžΆαžšαž’αž—αž·αžœαžŒαŸ’αžαž€αžΈαž‘αžΆαž”αžΆαž›αŸ‹αž‘αžΆαžαŸ‹ αž™αž»αžœαž‡αž“ αž“αž·αž„αž”αžšαž·αžŸαŸ’αžαžΆαž“
ever-recurrent
αž‘αžΎαž” αž˜αžΆαž“ αž€αžΆαžšαžαŸ’αžœαŸ‚αž„αž‚αŸ†αž“αž·αž αž‚αŸ’αž“αžΆ
αž…αž·αž“αž‘αžΆαŸ†αž„αž“αŸ„αŸ‡αŸ•αž–αŸαžαŸŒαž˜αžΆαž“αž’αž“αŸ’αžαžšαž‡αžΆαžαž·αž…αŸ‚αž€αžšαŸ†αž›αŸ‚αž€
តអ αž˜αžΏαž„ αžŸαŸ’αž›αžΆαž”αŸ‹ αž€αŸ
αž–αŸαžαŸŒαž˜αžΆαž“αž”αžΆαž“αž“αž·αž™αžΆαž™αžαžΆαž˜αž»αž“αž–αŸαž›αž€αžΎαž
αž‘αžΎαž”αžαŸ‚αž…αŸ’αžšαŸ€αž„αž“αŸ…αŸ’αŸ αŸ‘αŸ αž“αŸαŸ‡.αž”αžΎ
αž‡αžΆ αž—αžΆαžŸαžΆ αž”αžΆαžšαžΆαŸ†αž„ αž‚αŸ
αž αžΎαž™ αž™αžΆαž„αž˜αž€αž€αžΆαž“αŸ‹αžŸαŸ’αžαžΆαž“αž‘αžΈαž›αŸαž„
αž‚αž½αžšαžαŸ‚αžαŸ’αžšαžΌαžœαž”αžΆαž“αž…αžΆαžαŸ‹αž‘αž»αž€αžαžΆαž‡αžΆαž•αŸ’αž“αŸ‚αž€
waddles
αž“αŸαŸ‡αŸ” αžšαžΆαž„αž€αžΆαž™αžšαž”αžŸαŸ‹αž™αžΎαž„αž’αŸ’αžœαžΎαž€αžΆαžš
αž€αŸ’αžšαž…αŸαŸ‡αžœαž·αž‰αžŠαŸ„αž™αžŸαžΆαžšαžαŸ‚αž‡αŸ†αž“αž“αŸ‹αž˜αŸαž‚αž„αŸ’αž‚αž“αžΉαž„
αž“αŸƒαž€αžΆαžšαž’αž—αž·αžœαžŒαŸ’αžαžŸαŸαžŠαŸ’αž‹αž€αž·αž…αŸ’αž… αžŸαž„αŸ’αž‚αž˜αžšαž”αžŸαŸ‹αž”αŸ’αžšαž‘αŸαžŸ
αž€αžΌαž“ αž‡αžΆαž”αŸ‹ αž“αŸ…αž€αŸ’αž“αž»αž„ αžŠαŸƒ αž₯សី αžƒαžΎαž‰
αžαŸ’αž˜αŸ‚αžšαž“αŸαŸ‡ αž˜αžΆαž“αž–αŸ’αžšαŸ‡αž–αž„αŸ’αžŸαžΆαžœαžαžΆαžαŸ’αž˜αŸ‚αžšαžαŸ’αž›αŸ‡
αž“αŸ…αž€αžŽαŸ’αžαžΆαž›αž”αŸ’αžšαžŸαžΆαž‘αž˜αžΆαž“αž”αŸ’αžšαžΆαž„αŸ’αž‚αž˜αžΆαžŸ
αžαŸ†αžŽαŸ‚αž„αž‡αžΆαž‘αžΈαž”αŸ’αžšαžΉαž€αŸ’αžŸαžΆαžŸαž“αŸ’αžαž·αžŸαž»αžαž‡αžΆαžαž·αž€αŸ’αž“αž»αž„
αž‡αž”αŸ‰αž»αž“ αžαŸ’αžšαžΌαžœ αžˆαž”αŸ‹ αžŠαŸαž€ αž™αž›αŸ‹ αžŸαž”αŸ’αž
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αž€αžΎαžαžšαŸ„αž‚αž…αž˜αŸ’αž›αŸ‚αž€αž“αŸαŸ‡αžŸαŸ’αžαž·αžαž“αŸ…αž€αŸ’αž“αž»αž„
αžšαžΆαž”αŸ‹ αž–αžΆαž“αŸ‹ αž“αžΆαž€αŸ‹ αž–αžΈ
itself had set a bold new
αž”αžΆαž“αž˜αžΆαž“αž”αŸ’αžšαžŸαžΆαžŸαž“αŸαžαžΆ αžšαžΆαž›αŸ‹αž›αžΎαž€αžŠαŸ‚αž›
αž”αž“αŸ’αž‘αžΆαž”αŸ‹αž˜αž€αž‘αŸ€αžαž‘αž·αž“αŸ’αž“αž“αŸαž™αž“αŸαŸ‡αžαŸ’αžšαžΌαžœαž”αžΆαž“αž•αŸ’αžαž›αŸ‹
αž˜αž“αž»αžŸαŸ’αžŸαž˜αŸ’αž“αžΆαžŠαžΎαžšαž€αžΆαžαŸ‹ αžαŸ‚αž„αž—αžΆαž™
αžŠαŸ„αž™ αžαŸ’αž›αžΆαž… αžšαž’αŸ‚αž„ αž“αŸ„αŸ‡
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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 a path field
  • text: the transcription string for the image

The dataset currently contains:

  • train: 179,988 examples
  • val: 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": "αž αŸ’αž‚αžΈαžšαŸ‰αžΌαžŠ αž“αž·αž„ αž–αŸαž‡ αž’αž„αŸ’αž‚ αžŠαŸ‚αž›αž›αžΎαž€αž‘αžΎαž„",
}

Data Fields

  • image.bytes: Raw bytes of the image file.
  • image.path: Original image filename from labels.txt (for example train_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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