llm-jp/Synth-JDoc
Synth-JDoc Paper | Code Synth-JDoc is a dataset of synthetic Japanese document images generated using HTML/CSS. We generate embedded images, captions, and titles from prepared text, and use these elements to synthesize document images featuring diverse multi-column layouts in both vertical and horizontal writing. Because the document images are synthesized directly from text, this dataset is completely free from OCR errors. Dataset details id Image ID image… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/Synth-JDoc.
Synth-JDoc
Synth-JDoc is a dataset of synthetic Japanese document images generated using HTML/CSS.
We generate embedded images, captions, and titles from prepared text, and use these elements to synthesize document images featuring diverse multi-column layouts in both vertical and horizontal writing.
Because the document images are synthesized directly from text, this dataset is completely free from OCR errors.
Dataset details
id- Image ID
image- PIL Image
question- Prompt for VLM
text- The text written within the synthetic document image
is_vertivalTrueif the layout uses vertical writingnum_columns- Number of columns
noise_type- 0: No noise, 1: scan like noise, 2: Noise by Augraphy
This dataset was introduced in our paper: Synth-JDoc: Synthesizing a Japanese Document Image Dataset for OCR with Diverse Layouts and Embedded Images.
The code used to generate this dataset is available at our GitHub repository: https://github.com/llm-jp/synth-jdoc
Before releasing the dataset, we manually checked images flagged by an NSFW image detection model and removed those confirmed to be NSFW. Therefore, please note that this dataset is not identical to the one used for the experiments in our paper. Additionally, please be aware that this NSFW filtering process is not perfect.
License
This dataset is released under Creative Commons Attribution 4.0 License.
Citation
@InProceedings{10.1007/978-3-032-36033-5_15,
author="Sasagawa, Keito
and Kurita, Shuhei
and Kawahara, Daisuke",
editor="Fink, Gernot A.
and Forn{\'e}s, Alicia
and Kise, Koichi
and Lopresti, Daniel",
title="Synth-JDoc: Synthesizing a Japanese Document Image Dataset for OCR with Diverse Layouts and Embedded Images",
booktitle="Document Analysis and Recognition -- ICDAR 2026",
year="2027",
publisher="Springer Nature Switzerland",
address="Cham",
pages="245--260",
abstract="The ability of Large Vision Language Models (LVLMs) to read text within document images is crucial, as it enables various applications such as Document Visual Question Answering. To enhance the text-reading capabilities of LVLMs, high-quality OCR datasets are essential. This need is particularly critical for Japanese documents, which often feature vertically written text alongside horizontally written text. Current LVLMs demonstrate considerably lower performance on vertically written Japanese text than on horizontally written text, necessitating specialized OCR datasets to bridge this gap. However, manually constructing OCR datasets is expensive and difficult to scale. Alternatively, constructing datasets by extracting text from existing document images using OCR models introduces challenges, such as text recognition errors and the prerequisite of sourcing document images.",
isbn="978-3-032-36033-5"
}