Datasets:

Darayut
/

Textline-Detection-Dataset

license: cc-by-4.0
language:
  - km
  - en
task_categories:
  - object-detection
tags:
  - khmer
  - text-detection
  - ocr
  - document-analysis
  - yolo
  - synthetic
  - scene-text
pretty_name: Khmer Text Detection (Ultimate)
size_categories:
  - 10K<n<100K

Textline Detection — Ultimate Dataset

A large-scale, multi-source dataset for textline detection using YOLO-format bounding box annotations. Combines real scene text, document layout images, and synthetically generated Khmer document images.

Dataset Summary

Split	Images
Train	30,658
Val	2,764
Total	33,422

Classes

ID	Name	Description
0	`text_line`	A line of Khmer or mixed-script text
1	`image`	An embedded image/figure region within a document

Data Sources

1. Real Scene Text

Images captured in natural environments — street signs, storefronts, billboards, and handwritten Khmer documents. Derived from the DonkeySmall base dataset (~26K images).

2. Document Layout

Images from the DocLayNet multi-class document layout corpus, re-labelled for the two-class schema. Covers documents: official reports, newspapers, and books.

3. Synthetic Khmer Documents

Programmatically generated document images using custom Khmer text rendering pipelines. Fonts, sizes, backgrounds, and layouts are randomised. Labels are auto-generated (zero annotation cost).

Annotation Format

Labels follow YOLO v8 format — coordinates normalised to [0, 1]:

<class_id> <cx> <cy> <width> <height>

The annotations field is a JSON-serialised list:

[
  {"class_id": 0, "cx": 0.512, "cy": 0.234, "w": 0.310, "h": 0.045},
  {"class_id": 1, "cx": 0.720, "cy": 0.600, "w": 0.200, "h": 0.250}
]

Dataset Fields

Field	Type	Description
`image`	`Image`	Decoded PIL image
`image_path`	`string`	Original file path at collection time
`source`	`string`	`real_scene_text` / `doclaynet_khmer` / `synthetic_khmer_doc`
`split`	`string`	`train` or `val`
`annotations`	`string`	JSON list of YOLO bounding boxes
`num_objects`	`int32`	Number of annotated objects

Usage

from datasets import load_dataset

ds = load_dataset("Darayut/Multilingual-Textline-Detection-Dataset")

sample = ds["train"][0]
print(sample["source"], sample["num_objects"])
sample["image"].show()

Convert back to YOLO label files

import json
from pathlib import Path

def save_labels(split, out_dir):
    Path(out_dir).mkdir(parents=True, exist_ok=True)
    for row in ds[split]:
        stem = Path(row["image_path"]).stem
        anns = json.loads(row["annotations"])
        with open(f"{out_dir}/{stem}.txt", "w") as f:
            for a in anns:
                f.write(f"{a['class_id']} {a['cx']:.6f} {a['cy']:.6f} {a['w']:.6f} {a['h']:.6f}\n")

save_labels("train", "labels/train")
save_labels("val",   "labels/val")

YAML config for YOLOv8 training

train: /path/to/images/train
val:   /path/to/images/val
nc: 2
names:
  0: text_line
  1: image

Limitations

Synthetic images may not capture all real-world degradation (blur, skew, lighting variation).
Scene-text labels are semi-automatic and may have occasional missed detections.
Dataset is primarily Khmer script; other scripts appear only incidentally.

License

CC BY 4.0 — free to use, share, and adapt with attribution.