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three_column_index_tiny_font
Pulleyblank Outline p187: 3-column vocabulary index, tiny font, Chinese Vocabulary Items
178 Outline of Classical Chinese Grammar qi ... hù 其 ... 乎, wù hù 恶 乎, yè hù 也 乎, yú shì hù 於 是乎 hù zāi 乎 哉 exclamatory question 146, 262, 535 hú 胡 interrogative pronoun ‘why?’ 10, 91, 95, 96, 107, 142; in a proper name 78 hù 五 137 huò 或 ‘some, someone, something, perhaps’ 92, 130, 134, 135, 136, 79, 80, 156, 478, 479...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]
two_column_index
Pulleyblank Outline p197: General Index, two-column
General Index - Only the main occurrences of grammatical terms are indexed. ablaut 11 active 23, 27, 42, 43, 44, 122 adjective 9, 12, 13, 14, 23, 24-26, 27, 28, 36, 39, 42-44, 55, 62, 99, 100, 101 adnominal 71, 72, 97, 122, 125, 127, 130 adverb 9, 14, 58, 99, 100, 101, 115, 120, 121, 126, 141, 144, 151, 153, 155, 157,...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]
body_prose_wg_diacritics_cjk
Pulleyblank Outline p60: running prose with inline CJK + Wade-Giles diacritics + numbered examples
50 Outline of Classical Chinese Grammar scan 59 The phrase suǒ yǐ 所以 ‘that by which’ must always be given its full value in Classical Chinese. It does not have the meaning ‘therefore’ which it has acquired in the modern language. The expression shì yǐ 是以 ‘because of that, therefore,’ used as a sentence connective, in ...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]
monospace_dotmatrix_font
Carlitz Rhetoric p30: dot-matrix typewriter body prose, unusual font
24 The Rhetoric of Chin p'ing mei the transformation, hua, by the force of example, im- agined as a wind, feng). A desire to transform the morals of the age is expressed in prologues to certain plays early in the dynasty, but these are precisely the plays that later critics attacked for their contraven- tion of pen...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]
sparse_chapter_calibration
Carlitz Rhetoric p120: sparse chapter-opening page
6 Drama and Song: The Conventions Undermined The author of Chin p'ing mei must have loved drama and song, which he knew so well and used so fluently, but certain things suggest that he found them limited in diction, in characterization, and in the presentation of moral issues. This, after all, may be why he chose th...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]
dense_bilingual_dict_smallfont
Ming-Qing dialect dictionary p1: dense two-column bilingual (Chinese headword + citations) small font
A 阿 ①<副>用在动词前,表示疑问,对现在或将来的事发问。作用相当于“吗”。也可用在短语内。①同转头表示看见某事物后,或叫:“某某你,你听有黄山,阿有黄阳?”(山歌8卷)大家向前行一个村,看道阿能到黄龙。山歌9卷)这里是书房,阿知道是里,等我走进去。(才人篇9卷)阿想阿想他,房子未分看,铜钱未买不,阿看想他阿要得在里。(官场现形记10回)故歌借铜钱买来几件,随里去开几口,阿找阿找阿找阿几件儿。(海天鸿雪记8回)②<副>基本意义和①同,对过去的事发问,答有,有答有。(今一般用“啊”。③<副>重复意思,阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿。阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿阿)。阿阿阿阿...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]
dense_bilingual_dict_smallfont
Ming-Qing dialect dictionary p4: dense two-column bilingual small font
4 A 阿 养人。(原注:人犹善于养而厚于慈)(《诗》②《名》对已婚女性的尊称,有时用于自称。① 到,这些词皆含“到” 这个意思。阿,阿侍从,阿兄,阿叔,阿侄,阿姑,阿夫,阿叔父,阿叔母,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄弟,阿叔父兄弟,阿叔父兄弟,阿叔父兄弟,阿叔父兄弟,阿叔父兄弟,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父弟,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄,阿叔父兄弟,阿叔父兄,阿叔父兄,阿叔父...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]
bold_bilingual_dict_twocolumn
Hucker official-titles dictionary p218: two-column EN/CN terminology
feng-ch'ang ssu 1952-1969 212 feng-ch'ang ssu 1952-1969 1952 feng-ch'ang ssu 奉常寺 SUI-CH'ING: unofficial reference to the Court of Imperial Searches (Tai-ch'ang ssu); from 662 to 671, the of- ficial name of the Court. RR: cour des sacrifices impératifs. P27. 1953 feng-ch'ang t'ao-fu 奉常大夫 CH'ING: unofficial reference to ...
[{"model_id": "deepseek-ai/DeepSeek-OCR-2", "model_name": "DeepSeek-OCR-2", "column_name": "markdown", "timestamp": "2026-08-13T22:42:16.210914", "prompt_mode": "free", "max_tokens": 8192}]

Document OCR using DeepSeek-OCR-2

This dataset contains markdown-formatted OCR results from images in bokane/hucker-ocr-bakeoff-hard using DeepSeek-OCR-2.

Processing Details

Configuration

  • Image Column: image
  • Output Column: markdown
  • Dataset Split: train
  • Batch Size: 8
  • Max Model Length: 8,192 tokens
  • Max Output Tokens: 8,192
  • GPU Memory Utilization: 80.0%

Model Information

DeepSeek-OCR-2 is a 3B parameter vision-language model featuring Visual Causal Flow architecture for more human-like visual encoding. Building on DeepSeek-OCR v1, it offers enhanced document understanding with dynamic resolution up to (0-6)x768x768 + 1x1024x1024 patches.

Capabilities

  • LaTeX equations - Mathematical formulas preserved in LaTeX format
  • Tables - Extracted and formatted as HTML/markdown
  • Document structure - Headers, lists, and formatting maintained
  • Image grounding - Spatial layout and bounding box information
  • Complex layouts - Multi-column and hierarchical structures
  • Multilingual - Supports multiple languages

Dataset Structure

The dataset contains all original columns plus:

  • markdown: The extracted text in markdown format with preserved structure
  • 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 DeepSeek-OCR-2 vLLM script:

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \\
    bokane/hucker-ocr-bakeoff-hard \\
    <output-dataset> \\
    --image-column image

Performance

  • Processing Speed: ~0.0 images/second
  • Processing Method: Batch processing with vLLM (2-3x speedup over sequential)

Generated with UV Scripts

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