Datasets:

storytracer
/

dots-mocr-latin-test-output

image imagewidth (px) 1.77k 1.93k	markdown stringclasses 3 values	inference_info stringclasses 1 value
	MAGNUM BULLARIUM ROMANUM, SEU EJUSDEM CONTINUATIO, Quæ SUPPLEMENTI loco sit, tum huicce, tum aliis quæ præceſſerunt EDITIONIBUS ROMANÆ, & LUGDUNENSI. Accedunt, prout in Editione Romanâ, eorum Pontificum Vitæ, quorum Bullæ hinc recens prodeunt; Appendices inſuper ſuis quique locis aſſignati. CUM RUBRICIS, SUMMARIIS, ...	[{"model_id": "rednote-hilab/dots.mocr", "model_name": "dots.mocr", "column_name": "markdown", "timestamp": "2026-04-27T13:22:28.210230", "prompt_mode": "ocr", "temperature": 0.1, "top_p": 0.9, "max_tokens": 24000}]
	non fuerint actualiter poſſeſſæ usque ad diem Nati- vitatis Domini noſtri Jefu Chriſti proximè præteri- tum, à quo incipit annus præſens milleſimus qua- dringentesimus nonageſimus tertius; quando fue- rint per Nuntios & Capitaneos veſtros inventæ ali- quæ prædictarum Inſularum, auctoritate omnipo- tentis Dei nobis in B...	[{"model_id": "rednote-hilab/dots.mocr", "model_name": "dots.mocr", "column_name": "markdown", "timestamp": "2026-04-27T13:22:28.210230", "prompt_mode": "ocr", "temperature": 0.1, "top_p": 0.9, "max_tokens": 24000}]
	ANNO 1729. tos, etiam Cauſarum Palatii Apostolici Auditores judicari, & definiri debere, ac irritum, & ina- ne, ſi ſecus, ſuper his, à quoquam, quamvis auc- toritate ſcientèr, vel ignoranter contigerit atten- tari. Non obſtan. Constitutionibus, & Ordi- nationibus Apostolicis, ac quatenùs opus ſit, dicti Conservatorii,...	[{"model_id": "rednote-hilab/dots.mocr", "model_name": "dots.mocr", "column_name": "markdown", "timestamp": "2026-04-27T13:22:28.210230", "prompt_mode": "ocr", "temperature": 0.1, "top_p": 0.9, "max_tokens": 24000}]

Document OCR using dots.mocr

This dataset contains OCR results from images in /home/seb/data/ocr/latin-test-input/ using dots.mocr, a 3B multilingual model with SOTA document parsing and SVG generation.

Processing Details

Source Dataset: /home/seb/data/ocr/latin-test-input/
Model: rednote-hilab/dots.mocr
Number of Samples: 3
Processing Time: 2.9 min
Processing Date: 2026-04-27 13:22 UTC

Configuration

Image Column: image
Output Column: markdown
Dataset Split: train
Batch Size: 16
Prompt Mode: ocr
Max Model Length: 24,000 tokens
Max Output Tokens: 24,000
GPU Memory Utilization: 60.0%

Model Information

dots.mocr is a 3B multilingual document parsing model that excels at:

100+ Languages — Multilingual document support
Table extraction — Structured data recognition
Formulas — Mathematical notation preservation
Layout-aware — Reading order and structure preservation
Web screen parsing — Webpage layout analysis
Scene text spotting — Text detection in natural scenes
SVG code generation — Charts, UI layouts, scientific figures to SVG

Dataset Structure

The dataset contains all original columns plus:

markdown: The extracted text in markdown format
inference_info: JSON list tracking all OCR models applied to this dataset

Usage

from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("{output_dataset_id}", split="train")

# Access the markdown text
for example in dataset:
    print(example["markdown"])
    break

# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
    print(f"Column: {info['column_name']} - Model: {info['model_id']}")

Reproduction

This dataset was generated using the uv-scripts/ocr dots.mocr script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-mocr.py \
    /home/seb/data/ocr/latin-test-input/ \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --prompt-mode ocr \
    --max-model-len 24000 \
    --max-tokens 24000 \
    --gpu-memory-utilization 0.6

Generated with UV Scripts

Downloads last month: 10

Number of rows:

Total file size:

2.15 MB