| --- |
| 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: |
|
|
| ```json |
| [ |
| {"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 |
|
|
| ```python |
| 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 |
|
|
| ```python |
| 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 |
|
|
| ```yaml |
| 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](https://creativecommons.org/licenses/by/4.0/) — free to use, share, and adapt with attribution. |
|
|
| --- |
|
|
