High-precision, line-level bounding box and polygon annotations for the Manga109-s Dataset, supporting both full manga pages and speech bubble crops. Furigana is not labeled and is almost entirely excluded from line labels. Includes 8-point oriented polygons for slanted/rotated text lines. The annotation process is documented in METHODOLOGY.md (WIP).

Notice: This dataset contains zero dialogue text and zero images. It requires your own local copy of Manga109-s (or Manga109) to reconstruct dialogue text and process images.

Dataset Statistics

Metric	Count
Manga Books	87
Manga Pages	380
Speech Bubble Crops	5,988
Text Lines	14,182
Oriented Polygons	392
Total Characters	74,506
Average Lines / Bubble	2.37
Average Characters / Line	5.25

Quickstart

1. Prerequisites & Installation

Obtain an official copy of Manga109-s from Hugging Face (hal-utokyo/Manga109-s) (or the official Manga109 website; the 2026 version is recommended).

Ensure you have Python 3.8+. You can install the companion package or run directly with uv:

# Option A: Install as an editable package (provides the `manga109-lines` CLI)
pip install -e .
manga109-lines --help

# Option B: Run directly with uv (recommended, no manual install needed)
uv run manga109-lines --help
# or
uv run python build_dataset.py --help

Note: In all subsequent examples, manga109-lines and uv run python build_dataset.py can be used interchangeably.

2. Verify Alignment with Local Manga109-s

Check that your local Manga109-s files match the annotation geometry:

uv run python build_dataset.py verify --manga109-dir ./manga109s-v2026

3. Reconstruct Full Annotations with Dialogue Text

Fills in the official dialogue text from your local Manga109-s XMLs or CSV into a complete verified_data.json:

uv run python build_dataset.py reconstruct \
  --manga109-dir ./manga109s-v2026 \
  --output verified_data_reconstructed.json

Exporting to Machine Learning Formats

1. YOLO Format (Ultralytics YOLOv8 / YOLO11)

Mode A: Full Page Line Detection

Detects text lines across entire manga pages (images/{book}/{page}.jpg):

uv run python build_dataset.py export-yolo \
  --manga109-dir ./manga109s-v2026 \
  --target page \
  --task detect \
  --output-dir yolo_line_pages

Mode B: Bubble Crop Line Detection

Detects individual text lines within cropped speech bubbles (crops/{id}.png):

uv run python build_dataset.py export-yolo \
  --manga109-dir ./manga109s-v2026 \
  --target crop \
  --task detect \
  --output-dir yolo_line_crops

Options:

--task segment: Exports normalized 8-point polygon segmentations for oriented lines.
--include-images: Automatically copies or symlinks images into images/train and images/val.

2. COCO Instances JSON

Exports standard COCO instances JSON with text_line (category 1) and text_block (category 2):

# Page-level COCO
uv run python build_dataset.py export-coco \
  --manga109-dir ./manga109s-v2026 \
  --target page \
  --output manga109s_lines_coco_page.json

# Crop-level COCO
uv run python build_dataset.py export-coco \
  --manga109-dir ./manga109s-v2026 \
  --target crop \
  --output manga109s_lines_coco_crop.json

3. Enhanced Manga109 XML Files

Inserts <line index="..." xmin="..." ymin="..." xmax="..." ymax="..."> tags directly into the official Manga109 XML files:

uv run python build_dataset.py export-xml \
  --manga109-dir ./manga109s-v2026 \
  --output-dir manga109s_xml_with_lines

Sample output element:

<text id="00000d6f" xmin="192" ymin="957" xmax="263" ymax="1036">セリフ１\nセリフ２\nセリフ３
  <line index="0" xmin="236" ymin="957" xmax="257" ymax="1014">セリフ１</line>
  <line index="1" xmin="213" ymin="957" xmax="235" ymax="1036">セリフ２</line>
  <line index="2" xmin="192" ymin="957" xmax="211" ymax="1036">セリフ３</line>
</text>

4. Line OCR Dataset (JSONL)

Exports a line-level OCR mapping file for text recognition training:

uv run python build_dataset.py export-ocr \
  --manga109-dir ./manga109s-v2026 \
  --output manga109s_crops_ocr.jsonl

Data Schema (`line_annotations.json`)

{
  "images/ARMS/065.jpg": {
    "book": "ARMS",
    "page_index": 65,
    "width": 1654,
    "height": 1170,
    "texts": [
      {
        "id": "00000d6f",
        "xmin": 192,
        "ymin": 957,
        "xmax": 263,
        "ymax": 1036,
        "line_lengths": [3, 4],
        "lines": [
          {
            "line_index": 0,
            "xmin": 236,
            "ymin": 957,
            "xmax": 257,
            "ymax": 1014,
            "char_count": 3
          },
          {
            "line_index": 1,
            "xmin": 213,
            "ymin": 957,
            "xmax": 235,
            "ymax": 1036,
            "char_count": 4,
            "polygon": [213, 960, 230, 1036, 235, 1033, 218, 957]
          }
        ]
      }
    ]
  }
}

Key Fields

line_lengths: Array of character slice lengths for each line in reading order.
char_count: Number of characters corresponding to this line.
polygon: (Optional) 8-point coordinate array [x1, y1, x2, y2, x3, y3, x4, y4] specifying the oriented bounding box for tilted or slanted lines (present on 392 lines).

License & Citation

Annotation & Code License

The line annotations and accompanying tooling (build_dataset.py) are released under the MIT License.

Note: This license applies solely to the line annotation geometry files and associated utility code. The underlying Manga109-s dataset and artwork remain subject to the Manga109 Terms of Use.

Manga109 Terms of Use Notice

This release strictly abides by the Manga109-s Terms of Use. To use this dataset with original text or imagery, you must obtain a legitimate copy of Manga109-s from:

Citation

If you use these line annotations or conversion tools in your research, please cite this repository:

@misc{bluolightning2026manga109slines,
  author       = {Nav (bluolightning)},
  title        = {{Manga109-s Text Line Annotations}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/bluolightning/manga109s-line-annotations}}
}

Please also cite the underlying Manga109 / Manga109-s dataset and annotation papers:

@inproceedings{baek2026mangav26,
  title     = {{Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding}},
  author    = {Baek, Jeonghun and Miyai, Atsuyuki and Onohara, Shota and Ikuta, Hikaru and Aizawa, Kiyoharu},
  booktitle = {Culture × AI Workshop at ICML 2026},
  year      = {2026}
}

@article{multimedia_aizawa_2020,
  author  = {Aizawa, Kiyoharu and Fujimoto, Azuma and Otsubo, Atsushi and Ogawa, Toru and Matsui, Yusuke and Tsubota, Koki and Ikuta, Hikaru},
  title   = {Building a Manga Dataset ``{Manga109}'' with Annotations for Multimedia Applications},
  journal = {IEEE MultiMedia},
  volume  = {27},
  number  = {2},
  pages   = {8--18},
  doi     = {10.1109/mmul.2020.2987895},
  year    = {2020}
}

@article{mtap_matsui_2017,
  author  = {Matsui, Yusuke and Ito, Kota and Aramaki, Yuji and Fujimoto, Azuma and Ogawa, Toru and Yamasaki, Toshihiko and Aizawa, Kiyoharu},
  title   = {Sketch-based Manga Retrieval using {Manga109} Dataset},
  journal = {Multimedia Tools and Applications},
  volume  = {76},
  number  = {20},
  pages   = {21811--21838},
  doi     = {10.1007/s11042-016-4020-z},
  year    = {2017}
}

Downloads last month: 26

Number of rows:

Total file size:

4.12 MB

Manga109-s Text Line Annotations