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Datasets:
eridgd
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honmono-ocr-test

Tasks:
Image-to-Text
Modalities:
Image
Text
Formats:
imagefolder
Languages:
Japanese
Size:
10K - 100K
Tags:
ocr
japanese
text-recognition
book-ocr
scene-text
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metadata 
license: cc-by-4.0
task_categories:
  - image-to-text
language:
  - ja
tags:
  - ocr
  - japanese
  - text-recognition
  - book-ocr
  - scene-text
pretty_name: honmono-ocr test set
size_categories:
  - 10K<n<100K

honmono-ocr test set

A held-out benchmark of 14,256 real Japanese text lines, each a cropped line image paired with its transcription. It is the evaluation set for the honmono-ocr recognition models, intended both for reproducing their reported accuracy and for benchmarking other Japanese line-recognition models on the same data.

The lines are drawn from printed books, scanned print, and scene text, spanning the conditions a camera-based Japanese OCR system typically meets.

Dataset structure

A single test split of 14,256 examples. Each example has three fields:

field type description
image image one cropped text line
text string transcription, NFKC-normalized
source string origin: ndl_pdm, ndl_ndlocr, icdar_mlt, or ndl_oneline

Reported scores for the honmono-ocr models are computed against the NFKC-normalized text.

Composition by source:

source lines description
ndl_pdm 8,768 scanned pre-war printed books (National Diet Library)
icdar_mlt 4,114 Japanese scene text (signs, storefronts, displays)
ndl_ndlocr 1,353 National Diet Library print, more modern
ndl_oneline 21 a small NDL one-line set, reported for completeness

Both horizontal and vertical (縦書き) lines are included. Vertical crops are stored rotated 90°, the form the models consume at inference.

Usage 

from datasets import load_dataset

ds = load_dataset("eridgd/honmono-ocr-test", split="test")
print(ds[0]["text"], ds[0]["source"])
ds[0]["image"]

To reproduce the published honmono-ocr scores, use the evaluation runner in the code repository. This dataset bundles test_real.manifest.txt, which lists the same 14,256 lines:

python eval/run_eval.py \
  --model models/v6-small/honmono-v6-small-fp16.onnx \
  --manifest path/to/honmono-ocr-test/test_real.manifest.txt \
  --crops_dir path/to/honmono-ocr-test

Reference results, whole-line accuracy (a line counts as correct only when every character matches):

model overall NDL PDM NDL NDLOCR ICDAR MLT
honmono v6-small 83.5 87.5 83.2 75.2
honmono v6-tiny 73.6 80.7 77.8 57.3
PP-OCRv6-small (off-the-shelf) 50.2 50.8 48.9 49.6
PP-OCRv6-tiny (off-the-shelf) 26.2 29.8 22.3 19.8

Source data and licensing

This dataset is redistributed under CC-BY-4.0, the license of both source datasets, and contains material modified from the originals as noted below.

National Diet Library (NDL) OCR datasets (ndl_pdm, ndl_ndlocr, ndl_oneline). © National Diet Library, Japan. Licensed CC-BY-4.0. Source: https://lab.ndl.go.jp/

ICDAR Robust Reading Challenge on Multi-Lingual Scene Text — RRC-MLT, 2017 and 2019 (icdar_mlt). Licensed CC-BY-4.0. Source: https://rrc.cvc.uab.es/?ch=8 (2017) and https://rrc.cvc.uab.es/?ch=15 (2019). Dataset described in N. Nayef et al., ICDAR2019 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Recognition (RRC-MLT-2019), ICDAR 2019.

Modifications applied to both sources: restricted to Japanese text; images cropped to individual text-line regions; annotations converted to a single text field and NFKC-normalized; unusable samples removed. RRC-MLT-2019's test-set ground truth was not publicly released and is not included; the ICDAR lines are taken from the publicly released train/validation portions. No endorsement by the original creators is implied.

The NDL portion (approximately 10,000 lines) requires no registration. The ICDAR portion originates from the RRC site, which requires a free account to download the source data.

The ICDAR MLT portion consists of photographs of real-world scenes. CC-BY-4.0 grants the copyright and database rights held by the licensors; it does not address privacy, publicity, or trademark rights that may attach to people, signage, or logos visible in individual images. This is standard for scene-text datasets and does not restrict ordinary research or benchmarking use. Anyone reusing specific images in other contexts should evaluate those rights independently.

Citation

For the honmono-ocr models and this dataset packaging:

@software{honmono_ocr,
  title  = {honmono-ocr: on-device Japanese book OCR},
  author = {Evan Davis},
  year   = {2026},
  url    = {https://github.com/eridgd/honmono-ocr}
}

Users must also credit the source datasets (NDL Lab; ICDAR RRC-MLT) under their CC-BY-4.0 terms.

Links