dataset_info:
- config_name: board_vqa
features:
- name: subset
dtype: string
- name: image_id
dtype: string
- name: filename
dtype: string
- name: polygons
list:
- name: polygon_id
dtype: string
- name: polygon
list:
list: float64
- name: text
dtype: string
- name: direction
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
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dtype: string
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- name: fields
struct:
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struct:
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struct:
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struct:
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struct:
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struct:
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dtype: string
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struct:
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dtype: string
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list: string
- name: item_quantity
struct:
- name: value
dtype: string
- name: polygon_ids
list: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 2629852826
num_examples: 1025
download_size: 2577051472
dataset_size: 2629852826
- config_name: default
features:
- name: subset
dtype: string
- name: image_id
dtype: string
- name: filename
dtype: string
- name: polygons
list:
- name: polygon_id
dtype: string
- name: polygon
list:
list: float64
- name: text
dtype: string
- name: direction
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- name: question
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struct:
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struct:
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struct:
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- name: date
struct:
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struct:
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struct:
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dtype: string
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list: string
- name: image
dtype: image
splits:
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num_bytes: 8309148507
num_examples: 3241
download_size: 8855834580
dataset_size: 8309148507
- config_name: handwriting_ocr
features:
- name: subset
dtype: string
- name: image_id
dtype: string
- name: filename
dtype: string
- name: polygons
list:
- name: polygon_id
dtype: string
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list:
list: float64
- name: text
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list: string
- name: time
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dtype: string
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list: string
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list: string
- name: tax_amount
struct:
- name: value
dtype: string
- name: polygon_ids
list: string
- name: line_items
list:
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struct:
- name: value
dtype: string
- name: polygon_ids
list: string
- name: item_price
struct:
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dtype: string
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- name: item_quantity
struct:
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dtype: string
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list: string
- name: image
dtype: image
splits:
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num_bytes: 2547248088
num_examples: 1065
download_size: 2558935770
dataset_size: 2547248088
- config_name: receipt_kie
features:
- name: subset
dtype: string
- name: image_id
dtype: string
- name: filename
dtype: string
- name: polygons
list:
- name: polygon_id
dtype: string
- name: polygon
list:
list: float64
- name: text
dtype: string
- name: direction
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: evidence
list: string
- name: tool
dtype: string
- name: writer_id
dtype: int64
- name: fields
struct:
- name: store_name
struct:
- name: value
dtype: string
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list: string
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struct:
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dtype: string
- name: polygon_ids
list: string
- name: receipt_id
struct:
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dtype: string
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list: string
- name: date
struct:
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dtype: string
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list: string
- name: time
struct:
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dtype: string
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list: string
- name: total_amount
struct:
- name: value
dtype: string
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list: string
- name: tax_amount
struct:
- name: value
dtype: string
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list: string
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list:
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struct:
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dtype: string
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list: string
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struct:
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dtype: string
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list: string
- name: item_quantity
struct:
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dtype: string
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list: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 3842464017
num_examples: 1151
download_size: 3722180644
dataset_size: 3842464017
configs:
- config_name: board_vqa
data_files:
- split: train
path: board_vqa/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: handwriting_ocr
data_files:
- split: train
path: handwriting_ocr/train-*
- config_name: receipt_kie
data_files:
- split: train
path: receipt_kie/train-*
license: apache-2.0
language:
- ja
size_categories:
- 1K<n<10K
JaWildText
JaWildText is a Japanese scene text understanding benchmark for evaluating vision-language models (VLMs) on text-rich real-world images. It is designed to diagnose Japanese OCR, scene text visual question answering, and structured information extraction in practical conditions such as dense signboards, handwritten text, and mobile-captured receipts.
For details on the dataset construction, evaluation protocol, and baseline results, see the paper: JaWildText: A Benchmark for Vision-Language Models on Japanese Scene Text Understanding.
Dataset Summary
Japanese scene text poses challenges that are not fully captured by existing multilingual or document-centric benchmarks, including mixed writing systems, vertical writing, dense layouts, and a large character inventory. JaWildText provides three complementary evaluation tasks:
- Dense STVQA / Board VQA: question answering over text-rich signboards, posters, bulletin boards, and product/package images.
- Receipt KIE: key information extraction from real-world photographs of Japanese receipts.
- Handwriting OCR: page-level transcription of handwritten Japanese text across different writing media and directions.
The dataset contains 3,241 examples across the three task configurations. The default configuration is the union of all task subsets.
Configurations
| Configuration | Split | Examples | Description |
|---|---|---|---|
default |
train |
3,241 | All examples from the three task subsets |
board_vqa |
train |
1,025 | Dense scene text visual question answering |
handwriting_ocr |
train |
1,065 | Handwritten Japanese OCR |
receipt_kie |
train |
1,151 | Receipt key information extraction |
Although the dataset is distributed with a train split for compatibility with Hugging Face Datasets, JaWildText is intended as an evaluation benchmark.
Usage
from datasets import load_dataset
board_vqa = load_dataset("llm-jp/jawildtext", "board_vqa")
sample = board_vqa["train"][0]
image = sample["image"]
question = sample["question"]
answer = sample["answer"]
To load all examples:
from datasets import load_dataset
dataset = load_dataset("llm-jp/jawildtext", "default")
Data Fields
All configurations share a common schema. Fields that are not used by a particular task may be null, empty strings, or empty lists.
subset: subset name, such asboard_vqa,handwriting_ocr, orreceipt_kie.image_id: image identifier.filename: original image filename.image: input image.polygons: text-region annotations.polygon_id: unique text-region identifier within the example.polygon: polygon coordinates in image coordinates.text: annotated text string.direction: writing direction annotation when available.
question: question text for Dense STVQA / Board VQA examples.answer: reference answer for Dense STVQA / Board VQA examples.evidence: list ofpolygon_idvalues that provide the minimum textual evidence needed to answer the question.tool: writing medium/tool metadata for Handwriting OCR examples when available.writer_id: anonymized writer identifier for Handwriting OCR examples when available.fields: structured receipt fields for Receipt KIE examples.store_namestore_addressreceipt_iddatetimetotal_amounttax_amountline_items
Each receipt field contains a value and polygon_ids. The polygon_ids values refer to entries in polygons.
Task Details
Dense STVQA / Board VQA
The board_vqa configuration evaluates whether a model can read and reason over dense Japanese scene text. Each example contains an image, a natural-language question, a reference answer, and evidence region IDs. Questions are designed to require integrating information from one or more text regions rather than simply copying a single visible string.
Receipt KIE
The receipt_kie configuration evaluates structured extraction from Japanese receipt images. The target output is a JSON object containing header fields such as store name, date, time, total amount, and tax amount, as well as line-item information where available.
Handwriting OCR
The handwriting_ocr configuration evaluates page-level transcription of handwritten Japanese text. The subset includes multiple writing media and writing directions, including horizontal and vertical Japanese text.
Evaluation
We follow the evaluation protocol described in the JaWildText paper.
For Dense STVQA / Board VQA, models are prompted to enclose the final answer in \boxed{...}. The extracted answer is evaluated with judge-based accuracy: an LLM verifier compares the model prediction with the reference answer and returns a binary correctness label. Outputs that cannot be parsed receive a score of 0.
For Receipt KIE, models are prompted to output a single JSON object following the predefined schema. Outputs that cannot be parsed as JSON receive a score of 0. We report overall F1 over extracted fields and line items, and field-level accuracy for major header fields.
For Handwriting OCR, models output plain text transcriptions. We compute character-level similarity as max(0, 1 - CER), where CER is the Levenshtein distance between the prediction and reference divided by the reference length. Unicode NFKC normalization is applied before scoring.
The overall score is the unweighted average of Dense STVQA accuracy, Receipt KIE F1, and Handwriting OCR character-level similarity.
Intended Uses
JaWildText is intended for:
- evaluating Japanese scene text understanding in VLMs;
- evaluating Japanese OCR in real-world image conditions;
- evaluating text-centric visual question answering over Japanese scene text;
- evaluating receipt key information extraction and document understanding;
- diagnostic analysis of recognition, reasoning, formatting, and script-specific errors.
Out-of-Scope Uses
JaWildText should not be used for:
- identifying individuals, writers, stores, or customers;
- inferring personal attributes or purchasing behavior;
- surveillance or profiling;
- treating visible trademarks, store names, or product names as endorsement by the rightsholders;
- training or deploying systems in high-stakes settings without additional validation.
Limitations and Ethical Considerations
JaWildText consists of real-world Japanese images and may contain store names, addresses, dates, prices, product names, logos, signs, and other third-party visual information. The dataset is released under Apache-2.0, but the license does not grant trademark rights or imply endorsement by any third-party entities visible in the images.
The dataset reflects images collected in Japan and should not be assumed to represent all Japanese text usage, all receipt formats, or all handwriting styles. Image quality, perspective, lighting, occlusion, and layout complexity vary across examples.
If you identify privacy-sensitive content or other issues in the dataset, please contact the maintainers.
TODO: add contact / takedown address.
License
JaWildText, including both annotations/metadata and images, is released under the Apache License 2.0.
Citation
If you use JaWildText, please cite:
@inproceedings{maeda-etal-2026-jawildtext,
title = {{JaWildText}: A Benchmark for Vision-Language Models on Japanese Scene Text Understanding},
author = {Maeda, Koki and Okazaki, Naoaki},
booktitle = {Proceedings of the 20th International Conference on Document Analysis and Recognition (ICDAR)},
year = {2026},
note = {To appear}
}