{
  "meta": {
    "pretty_name": "OCRBench",
    "dataset_id": "evalscope/OCRBench",
    "paper_url": null,
    "tags": [
      "MultiModal",
      "Knowledge",
      "QA"
    ],
    "metrics": [
      "acc"
    ],
    "few_shot_num": 0,
    "eval_split": "test",
    "train_split": "",
    "subset_list": [
      "Regular Text Recognition",
      "Irregular Text Recognition",
      "Artistic Text Recognition",
      "Handwriting Recognition",
      "Digit String Recognition",
      "Non-Semantic Text Recognition",
      "Scene Text-centric VQA",
      "Doc-oriented VQA",
      "Key Information Extraction",
      "Handwritten Mathematical Expression Recognition"
    ],
    "description": "\n## Overview\n\nOCRBench is a comprehensive evaluation benchmark designed to assess the OCR (Optical Character Recognition) capabilities of Large Multimodal Models. It covers five key OCR-related tasks with 1,000 manually verified question-answer pairs.\n\n## Task Description\n\n- **Task Type**: OCR and Document Understanding\n- **Input**: Image with OCR-related question\n- **Output**: Text recognition or extraction result\n- **Components**: Text Recognition, VQA, Document VQA, Key Info Extraction, Math Expression Recognition\n\n## Key Features\n\n- 1,000 question-answer pairs across 10 categories\n- Manually verified and corrected answers\n- Categories include: Regular/Irregular/Artistic/Handwriting Text Recognition\n- Scene Text-centric VQA and Document-oriented VQA\n- Key Information Extraction\n- Handwritten Mathematical Expression Recognition (HME100k)\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Simple accuracy metric (inclusion-based matching)\n- Results broken down by question type/category\n- Different matching rules for HME100k (space-insensitive)\n- Comprehensive test of OCR capabilities in multimodal models\n",
    "prompt_template": "{question}",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "vlm"
  },
  "statistics": {
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        "name": "Regular Text Recognition",
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        "name": "Doc-oriented VQA",
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        "prompt_length_min": 21,
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        "name": "Key Information Extraction",
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        "name": "Handwritten Mathematical Expression Recognition",
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        "prompt_length_mean": 79,
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    "target_length_mean": 21.86,
    "computed_at": "2026-01-28T11:15:39.297277",
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  "sample_example": {
    "data": {
      "input": [
        {
          "id": "ff23a835",
          "content": [
            {
              "text": "what is written in the image?"
            },
            {
              "image": "[BASE64_IMAGE: jpeg, ~1.2KB]"
            }
          ]
        }
      ],
      "target": "[\"CENTRE\"]",
      "id": 0,
      "group_id": 0,
      "subset_key": "Regular Text Recognition",
      "metadata": {
        "dataset": "IIIT5K",
        "question_type": "Regular Text Recognition"
      }
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    "subset": "Regular Text Recognition",
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  "readme": {
    "en": "# OCRBench\n\n\n## Overview\n\nOCRBench is a comprehensive evaluation benchmark designed to assess the OCR (Optical Character Recognition) capabilities of Large Multimodal Models. It covers five key OCR-related tasks with 1,000 manually verified question-answer pairs.\n\n## Task Description\n\n- **Task Type**: OCR and Document Understanding\n- **Input**: Image with OCR-related question\n- **Output**: Text recognition or extraction result\n- **Components**: Text Recognition, VQA, Document VQA, Key Info Extraction, Math Expression Recognition\n\n## Key Features\n\n- 1,000 question-answer pairs across 10 categories\n- Manually verified and corrected answers\n- Categories include: Regular/Irregular/Artistic/Handwriting Text Recognition\n- Scene Text-centric VQA and Document-oriented VQA\n- Key Information Extraction\n- Handwritten Mathematical Expression Recognition (HME100k)\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Simple accuracy metric (inclusion-based matching)\n- Results broken down by question type/category\n- Different matching rules for HME100k (space-insensitive)\n- Comprehensive test of OCR capabilities in multimodal models\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `ocr_bench` |\n| **Dataset ID** | [evalscope/OCRBench](https://modelscope.cn/datasets/evalscope/OCRBench/summary) |\n| **Paper** | N/A |\n| **Tags** | `Knowledge`, `MultiModal`, `QA` |\n| **Metrics** | `acc` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 1,000 |\n| Prompt Length (Mean) | 55.78 chars |\n| Prompt Length (Min/Max) | 14 / 149 chars |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `Regular Text Recognition` | 50 | 29 | 29 | 29 |\n| `Irregular Text Recognition` | 50 | 29 | 29 | 29 |\n| `Artistic Text Recognition` | 50 | 29 | 29 | 29 |\n| `Handwriting Recognition` | 50 | 29 | 29 | 29 |\n| `Digit String Recognition` | 50 | 32 | 32 | 32 |\n| `Non-Semantic Text Recognition` | 50 | 29 | 29 | 29 |\n| `Scene Text-centric VQA` | 200 | 34.6 | 14 | 101 |\n| `Doc-oriented VQA` | 200 | 59 | 21 | 136 |\n| `Key Information Extraction` | 200 | 101.58 | 86 | 149 |\n| `Handwritten Mathematical Expression Recognition` | 100 | 79 | 79 | 79 |\n\n**Image Statistics:**\n\n| Metric | Value |\n|--------|-------|\n| Total Images | 1,000 |\n| Images per Sample | min: 1, max: 1, mean: 1 |\n| Resolution Range | 25x16 - 4961x7016 |\n| Formats | jpeg |\n\n\n## Sample Example\n\n**Subset**: `Regular Text Recognition`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"ff23a835\",\n      \"content\": [\n        {\n          \"text\": \"what is written in the image?\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: jpeg, ~1.2KB]\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"[\\\"CENTRE\\\"]\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"Regular Text Recognition\",\n  \"metadata\": {\n    \"dataset\": \"IIIT5K\",\n    \"question_type\": \"Regular Text Recognition\"\n  }\n}\n```\n\n## Prompt Template\n\n**Prompt Template:**\n```text\n{question}\n```\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets ocr_bench \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['ocr_bench'],\n    dataset_args={\n        'ocr_bench': {\n            # subset_list: ['Regular Text Recognition', 'Irregular Text Recognition', 'Artistic Text Recognition']  # optional, evaluate specific subsets\n        }\n    },\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# OCRBench\n\n## 概述\n\nOCRBench 是一个综合评估基准，旨在评估大语言多模态模型的 OCR（光学字符识别）能力。它涵盖五个关键 OCR 相关任务，包含 1,000 个经过人工验证的问题-答案对。\n\n## 任务描述\n\n- **任务类型**：OCR 与文档理解  \n- **输入**：包含 OCR 相关问题的图像  \n- **输出**：文本识别或提取结果  \n- **组成部分**：文本识别、视觉问答（VQA）、文档导向的 VQA、关键信息提取、数学表达式识别  \n\n## 主要特点\n\n- 覆盖 10 个类别的 1,000 个问答对  \n- 答案经过人工验证和修正  \n- 类别包括：常规/不规则/艺术字体/手写文本识别  \n- 以场景文本为中心的 VQA 和面向文档的 VQA  \n- 关键信息提取  \n- 手写数学表达式识别（HME100k）\n\n## 评估说明\n\n- 默认配置使用 **0-shot** 评估  \n- 使用简单准确率指标（基于包含匹配）  \n- 结果按问题类型/类别细分  \n- HME100k 使用不同的匹配规则（忽略空格）  \n- 全面测试多模态模型中的 OCR 能力  \n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `ocr_bench` |\n| **数据集 ID** | [evalscope/OCRBench](https://modelscope.cn/datasets/evalscope/OCRBench/summary) |\n| **论文** | N/A |\n| **标签** | `Knowledge`, `MultiModal`, `QA` |\n| **指标** | `acc` |\n| **默认示例数** | 0-shot |\n| **评估划分** | `test` |\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 1,000 |\n| 提示词长度（平均） | 55.78 字符 |\n| 提示词长度（最小/最大） | 14 / 149 字符 |\n\n**各子集统计信息：**\n\n| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |\n|--------|---------|-------------|------------|------------|\n| `Regular Text Recognition` | 50 | 29 | 29 | 29 |\n| `Irregular Text Recognition` | 50 | 29 | 29 | 29 |\n| `Artistic Text Recognition` | 50 | 29 | 29 | 29 |\n| `Handwriting Recognition` | 50 | 29 | 29 | 29 |\n| `Digit String Recognition` | 50 | 32 | 32 | 32 |\n| `Non-Semantic Text Recognition` | 50 | 29 | 29 | 29 |\n| `Scene Text-centric VQA` | 200 | 34.6 | 14 | 101 |\n| `Doc-oriented VQA` | 200 | 59 | 21 | 136 |\n| `Key Information Extraction` | 200 | 101.58 | 86 | 149 |\n| `Handwritten Mathematical Expression Recognition` | 100 | 79 | 79 | 79 |\n\n**图像统计信息：**\n\n| 指标 | 值 |\n|--------|-------|\n| 图像总数 | 1,000 |\n| 每样本图像数 | 最小: 1, 最大: 1, 平均: 1 |\n| 分辨率范围 | 25x16 - 4961x7016 |\n| 格式 | jpeg |\n\n## 样例示例\n\n**子集**: `Regular Text Recognition`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"ff23a835\",\n      \"content\": [\n        {\n          \"text\": \"what is written in the image?\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: jpeg, ~1.2KB]\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"[\\\"CENTRE\\\"]\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"Regular Text Recognition\",\n  \"metadata\": {\n    \"dataset\": \"IIIT5K\",\n    \"question_type\": \"Regular Text Recognition\"\n  }\n}\n```\n\n## 提示模板\n\n**提示模板：**\n```text\n{question}\n```\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets ocr_bench \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['ocr_bench'],\n    dataset_args={\n        'ocr_bench': {\n            # subset_list: ['Regular Text Recognition', 'Irregular Text Recognition', 'Artistic Text Recognition']  # 可选，用于评估特定子集\n        }\n    },\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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  "updated_at": "2026-01-28T17:31:32.380405",
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