Datasets:
image imagewidth (px) 566 1.13k | image_id stringlengths 13 13 | split stringclasses 1
value | width int32 566 1.13k | height int32 640 1.25k | annotations dict |
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kh_doc_000300 | train | 816 | 1,056 | {
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kh_doc_000301 | train | 800 | 1,130 | {
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kh_doc_000302 | train | 566 | 800 | {
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kh_doc_000303 | train | 1,130 | 800 | {
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kh_doc_000304 | train | 900 | 640 | {
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kh_doc_000305 | train | 720 | 960 | {
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kh_doc_000306 | train | 640 | 1,000 | {
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kh_doc_000307 | train | 800 | 1,130 | {
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kh_doc_000308 | train | 900 | 640 | {
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kh_doc_000309 | train | 800 | 1,130 | {
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kh_doc_000310 | train | 1,130 | 800 | {
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kh_doc_000311 | train | 600 | 850 | {
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kh_doc_000312 | train | 900 | 640 | {
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kh_doc_000313 | train | 850 | 1,180 | {
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kh_doc_000314 | train | 1,130 | 800 | {
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kh_doc_000315 | train | 800 | 1,130 | {
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kh_doc_000316 | train | 1,130 | 800 | {
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kh_doc_000317 | train | 1,056 | 816 | {
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kh_doc_000318 | train | 600 | 850 | {
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kh_doc_000319 | train | 640 | 1,000 | {
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kh_doc_000320 | train | 1,130 | 800 | {
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kh_doc_000321 | train | 640 | 1,000 | {
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kh_doc_000322 | train | 816 | 1,056 | {
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kh_doc_000323 | train | 640 | 1,000 | {
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kh_doc_000324 | train | 600 | 850 | {
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kh_doc_000325 | train | 850 | 1,180 | {
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kh_doc_000326 | train | 1,130 | 800 | {
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kh_doc_000327 | train | 640 | 1,000 | {
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kh_doc_000328 | train | 800 | 1,130 | {
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kh_doc_000329 | train | 1,130 | 800 | {
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kh_doc_000330 | train | 850 | 1,180 | {
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kh_doc_000331 | train | 816 | 1,056 | {
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kh_doc_000332 | train | 950 | 1,250 | {
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kh_doc_000333 | train | 760 | 1,075 | {
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kh_doc_000334 | train | 1,056 | 816 | {
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kh_doc_000335 | train | 816 | 1,056 | {
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kh_doc_000336 | train | 900 | 640 | {
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kh_doc_000337 | train | 900 | 640 | {
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kh_doc_000338 | train | 720 | 960 | {
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kh_doc_000339 | train | 1,056 | 816 | {
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kh_doc_000340 | train | 950 | 1,250 | {
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kh_doc_000341 | train | 640 | 1,000 | {
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kh_doc_000342 | train | 900 | 640 | {
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kh_doc_000343 | train | 760 | 1,075 | {
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kh_doc_000345 | train | 816 | 1,056 | {
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kh_doc_000346 | train | 640 | 1,000 | {
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kh_doc_000347 | train | 800 | 1,130 | {
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kh_doc_000348 | train | 1,056 | 816 | {
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kh_doc_000349 | train | 950 | 1,250 | {
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kh_doc_000350 | train | 950 | 1,250 | {
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kh_doc_000351 | train | 900 | 640 | {
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kh_doc_000352 | train | 600 | 850 | {
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kh_doc_000353 | train | 800 | 1,130 | {
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kh_doc_000354 | train | 760 | 1,075 | {
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kh_doc_000355 | train | 1,056 | 816 | {
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kh_doc_000358 | train | 720 | 960 | {
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kh_doc_000359 | train | 950 | 1,250 | {
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kh_doc_000360 | train | 640 | 1,000 | {
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kh_doc_000361 | train | 640 | 1,000 | {
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kh_doc_000362 | train | 950 | 1,250 | {
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kh_doc_000363 | train | 816 | 1,056 | {
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kh_doc_000364 | train | 720 | 960 | {
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kh_doc_000365 | train | 816 | 1,056 | {
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kh_doc_000366 | train | 720 | 960 | {
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kh_doc_000369 | train | 760 | 1,075 | {
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kh_doc_000370 | train | 850 | 1,180 | {
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kh_doc_000371 | train | 1,130 | 800 | {
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kh_doc_000372 | train | 1,056 | 816 | {
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kh_doc_000373 | train | 800 | 1,130 | {
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kh_doc_000374 | train | 900 | 640 | {
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kh_doc_000375 | train | 850 | 1,180 | {
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kh_doc_000376 | train | 566 | 800 | {
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kh_doc_000377 | train | 816 | 1,056 | {
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kh_doc_000378 | train | 950 | 1,250 | {
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kh_doc_000379 | train | 850 | 1,180 | {
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kh_doc_000380 | train | 800 | 1,130 | {
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kh_doc_000381 | train | 566 | 800 | {
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kh_doc_000382 | train | 800 | 1,130 | {
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kh_doc_000383 | train | 640 | 1,000 | {
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kh_doc_000384 | train | 1,056 | 816 | {
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kh_doc_000385 | train | 816 | 1,056 | {
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kh_doc_000386 | train | 900 | 640 | {
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kh_doc_000387 | train | 900 | 640 | {
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kh_doc_000388 | train | 950 | 1,250 | {
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kh_doc_000389 | train | 760 | 1,075 | {
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kh_doc_000390 | train | 850 | 1,180 | {
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kh_doc_000391 | train | 1,056 | 816 | {
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kh_doc_000392 | train | 566 | 800 | {
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kh_doc_000393 | train | 950 | 1,250 | {
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kh_doc_000394 | train | 950 | 1,250 | {
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End of preview. Expand in Data Studio
Synthetic Khmer Document Text-Line Detection Dataset
Synthetic dataset for single-class text-line detection on Cambodian (Khmer) official documents — press releases, ministry letters, formal memos.
Generated with a procedural Pillow-based pipeline featuring:
- 8 layout templates (standard, letter, announcement, report, sparse, two-column, memo, plain)
- 12 page sizes from A5 to A4-landscape
- Variable margins, font sizes, line spacing, and indentation
- Photometric augmentations (brightness, blur, noise, JPEG, shadow, vignette, fold/crease)
Class
| ID | Name | Description |
|---|---|---|
| 0 | text_line |
Any horizontal line of text |
Dataset statistics
| Split | Images |
|---|---|
| train | 1732 |
| val | 300 |
Schema
{
"image": Image(),
"image_id": Value("string"), # e.g. "kh_doc_000042"
"split": Value("string"), # "train" | "val"
"width": Value("int32"),
"height": Value("int32"),
"annotations": Sequence({
"bbox": Sequence(Value("float32"), length=4), # [cx,cy,w,h] normalised
"cls_id": Value("int32"), # always 0
}),
}
Load with HF Datasets
from datasets import load_dataset
ds = load_dataset("Darayut/khmer-textline-detection")
sample = ds["train"][0]
print(sample["image"]) # PIL Image
print(sample["annotations"]) # dict of lists
Raw YOLO files
data/yolo_raw.zip contains the native YOLO directory layout
(images/, labels/, dataset.yaml) for direct Ultralytics training:
from huggingface_hub import hf_hub_download
import zipfile, pathlib
zip_path = hf_hub_download(
repo_id = "Darayut/khmer-textline-detection",
filename = "data/yolo_raw.zip",
repo_type = "dataset",
)
with zipfile.ZipFile(zip_path) as zf:
zf.extractall("./khmer_doc_yolo")
# yolo train data=khmer_doc_yolo/dataset.yaml model=yolo11n.pt
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