Datasets:

storytracer
/

dots-mocr-latin-test-layout

image imagewidth (px) 1.77k 1.93k	markdown stringclasses 3 values	inference_info stringclasses 1 value
	[{"bbox": [74, 223, 1529, 1015], "category": "Title", "text": "MAGNUM\nBULLARIUM\nROMANUM,\nSEU EJUSDEM\nCONTINUATIO,"}, {"bbox": [79, 1059, 1529, 1154], "category": "Text", "text": "Quæ SUPPLEMENTI loco sit, tum huicce, tum aliis quæ præceſſerunt EDITIONIBUS ROMANÆ, & LUGDUNENSI."}, {"bbox": [79, 1182, 1529, 1288], "c...	[{"model_id": "rednote-hilab/dots.mocr", "model_name": "dots.mocr", "column_name": "markdown", "timestamp": "2026-04-27T13:35:43.301418", "prompt_mode": "layout-all", "temperature": 0.1, "top_p": 0.9, "max_tokens": 24000}]
	[{"bbox": [11, 298, 118, 367], "category": "Page-header", "text": "ANNO\n1493."}, {"bbox": [426, 298, 950, 343], "category": "Page-header", "text": "ALEXANDER SEXTUS."}, {"bbox": [1235, 321, 1256, 361], "category": "Page-header", "text": "3"}, {"bbox": [1307, 312, 1407, 382], "category": "Page-header", "text": "ANNO\n1...	[{"model_id": "rednote-hilab/dots.mocr", "model_name": "dots.mocr", "column_name": "markdown", "timestamp": "2026-04-27T13:35:43.301418", "prompt_mode": "layout-all", "temperature": 0.1, "top_p": 0.9, "max_tokens": 24000}]
	[{"bbox": [444, 343, 926, 375], "category": "Page-header", "text": "BENEDICTUS XIII."}, {"bbox": [1205, 348, 1258, 381], "category": "Page-header", "text": "405"}, {"bbox": [38, 396, 147, 467], "category": "Text", "text": "ANNO\n1729."}, {"bbox": [165, 396, 702, 1188], "category": "Text", "text": "tos, etiam Cauſarum P...	[{"model_id": "rednote-hilab/dots.mocr", "model_name": "dots.mocr", "column_name": "markdown", "timestamp": "2026-04-27T13:35:43.301418", "prompt_mode": "layout-all", "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: 3.3 min
Processing Date: 2026-04-27 13:35 UTC

Configuration

Image Column: image
Output Column: markdown
Dataset Split: train
Batch Size: 16
Prompt Mode: layout-all
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 layout-all \
    --max-model-len 24000 \
    --max-tokens 24000 \
    --gpu-memory-utilization 0.6

Generated with UV Scripts

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