Datasetpublic

Darayut/khmer-textline-dataset

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… See the full description on the dataset page: https://huggingface.co/datasets/Darayut/khmer-textline-dataset.

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Dataset Card

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	1955
val	345

Schema

python

{
    "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

python

from datasets import load_dataset
ds = load_dataset("Darayut/khmer-textline-dataset")
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:

python

from huggingface_hub import hf_hub_download
import zipfile, pathlib

zip_path = hf_hub_download(
    repo_id   = "Darayut/khmer-textline-dataset",
    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