Darayut/Textline-Detection-Dataset

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.

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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

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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).

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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}
]

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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

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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

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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.

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License

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

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