doc-decision-bench

From document understanding to document decisions.

608 images · 2,617 Chinese–English question pairs · 5,234 language records

English · 简体中文

📄 A benchmark for document understanding and decisions

Understanding a document means being able to answer questions, check information and make judgments based on its content. doc-decision-bench is a bilingual Chinese–English multiple-choice benchmark for document images. It compares models on document recognition, content understanding, information verification and rule-based judgments.

The benchmark uses a unified input format: document image + question + candidates → answer label. Decision conditions and candidates are explicitly provided so that different models can be evaluated on the same inputs.

Each image can have multiple questions, with different candidate content and counts. Chinese and English versions are paired through pair_id, supporting analysis by language, task and document category.

🔎 Evaluation

Capability	What is evaluated
Document recognition and classification	Identifying a document's domain, category, purpose and hierarchical relationships
Visual attributes and layout understanding	Recognizing orientation, layout, document regions and visible elements
Fields and content relationships	Understanding fields and comparing relationships among numbers, text and table contents
Consistency and completeness	Checking whether information is consistent and whether specified materials or fields are present
Conditional reasoning and rule-based judgments	Combining document facts under explicitly provided conditions
Workflow routing and action selection	Selecting a processing category or next action based on document facts and question-level rules

These tasks evaluate understanding of images and the conditions stated in questions. Business rules are provided in the questions; models are not expected to infer the approval policies of real organizations.

📊 Dataset composition

Category	Images	Bilingual question pairs	Language records
Document understanding and decisions	388	1,848	3,696
Cross-industry classification	220	769	1,538

• Document understanding and decisions: covers document categories, field understanding and relationships, layout, content consistency, completeness, conditional reasoning, workflow routing, action selection and orientation recognition.
• Cross-industry classification: covers a document's domain, type, hierarchical category and purpose.

Chinese and English each contain 2,617 records. The total of 5,234 counts language records, not 5,234 independent questions.

🗂️ Repository structure

doc-decision-bench/
├── README.md
├── README.zh-CN.md
├── data/
│   └── data.jsonl
└── images/
    ├── 000001.jpg
    ├── 000002.jpg
    ├── ...
    └── 000608.png

Images retain their original JPG or PNG format. Each line in data/data.jsonl contains one question record. Multiple questions about the same image share one image file.

Image paths are relative to the directory containing data.jsonl, so records use paths such as ../images/000001.jpg. Reusing image files across questions avoids duplicate storage.

📥 Download and read the dataset

Download

hf download Immortal-Zhang/doc-decision-bench \
  --repo-type dataset \
  --local-dir doc-decision-bench

Read records and resolve image paths

import json
from pathlib import Path

jsonl_path = Path("doc-decision-bench/data/data.jsonl")

with jsonl_path.open(encoding="utf-8") as f:
    records = [json.loads(line) for line in f if line.strip()]

record = records[0]
image_path = (jsonl_path.parent / record["image"]).resolve()
question = record["question"]["instructions"]
candidates = record["question"]["criteria"]
reference_label = record["label"]

print(image_path)
print(question)
print(candidates)

🧩 Data format

Each record contains only 7 top-level fields: id, image, pair_id, language, subset, question and label. The example below is the English counterpart of the first record. Each record occupies a single line in the JSONL file:

{
  "id": "000001_01_en",
  "image": "../images/000001.jpg",
  "pair_id": "000001_01",
  "language": "en",
  "subset": "document_decision",
  "question": {
    "instructions": "What is the main explanatory purpose of this excerpt?",
    "criteria": {
      "A": "To compare the performance of two processing methods",
      "B": "To specify the acceptance criteria for a processing method",
      "C": "To define a processing method and the objects it is used to process",
      "D": "To list the operating steps of a processing method"
    }
  },
  "label": "C"
}

Field	Meaning
id	Unique identifier for each language record
image	Image path relative to the directory containing the JSONL file
pair_id	Identifier shared by the Chinese and English versions of the same question
language	zh for Chinese or en for English
subset	document_decision or industry_classification
question	Question object; instructions contains the question and decision rules, while criteria contains ordered candidate labels and text
label	Reference answer label, matching a key in question.criteria

🎯 Dataset categories and evaluation

Category	Images	Bilingual question pairs	Language records
Document understanding and decisions	388	1,848	3,696
Cross-industry classification	220	769	1,538

All records are stored in one data.jsonl file. Filter by subset: document_decision denotes document understanding and decisions, and industry_classification denotes cross-industry classification.

Data quality and evaluation boundaries

All reference answers have been manually reviewed.

License and source information

The annotation license in the complete source records is Apache 2.0.