{
  "meta": {
    "pretty_name": "Maritime-OCR-Bench",
    "dataset_id": "HiDolphin/MaritimeOCRBench",
    "paper_url": null,
    "tags": [
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      "QA"
    ],
    "metrics": [
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    "few_shot_num": 0,
    "eval_split": "test",
    "train_split": "",
    "subset_list": [
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      "VQA",
      "parsing",
      "json1",
      "json2"
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    "description": "\n## Overview\n\nMaritime-OCR-Bench is a comprehensive evaluation benchmark for assessing multimodal large model capabilities\non OCR-related tasks. The current released set contains 1,888 manually curated samples across five task types.\n\n## Task Types\n\n- **VQA**: Visual question answering on document/scene images\n- **IE**: Information extraction requiring strict JSON output\n- **parsing**: Text recognition and parsing from images\n- **json1**: Text spotting with JSON v1 structured output\n- **json2**: Text spotting with JSON v2 structured output\n\n## Evaluation Metrics\n\nEach task type uses a specialized scoring method:\n- VQA/parsing: Multi-dimensional text similarity (edit distance, char F1, LCS F1, table-aware similarity)\n- IE: Text coverage + JSON strictness (0.5 * coverage + 0.5 * json_strict)\n- json1/json2: DIoU layout score + text score (0.7 * diou + 0.3 * text)\n",
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    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "vlm"
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    "computed_at": "2026-06-04T11:17:32.943703",
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    "data": {
      "input": [
        {
          "id": "1d6ce119",
          "content": [
            {
              "text": "请提取所有关键信息，并以 JSON 格式返回。"
            },
            {
              "image": "[BASE64_IMAGE: png, ~550.9KB]"
            }
          ]
        }
      ],
      "target": "{\n  \"Document ID\": \"WiCE Error Message Description\",\n  \"Revision\": \"REV 1\",\n  \"Date\": \"2024-09-12\",\n  \"Company_Logo_Text\":\"WIN GD\",\n  \"Error Messages\": [\n    {\n      \"ID Number\": \"COFD-52\",\n      \"Designation\": \"Fuel Pump Control Signal #2 Fa ... [TRUNCATED 1668 chars] ... nt must be within 4 ~ 20mA.\\n• If necessary, the sensor can be replaced with new one (Caution: before dismantling, do depressurize rail)\"\n    }\n  ],\n  \"Footer\": \"T_PC-Drawing_Portrait | Release: 3.10 (2024-05-15)\",\n  \"Page\": \"Page 20 of 83\"\n}",
      "id": 0,
      "group_id": 0,
      "subset_key": "IE",
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        "task_type": "IE",
        "prompt": "<image>请提取所有关键信息，并以 JSON 格式返回。",
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    "subset": "IE",
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  "readme": {
    "en": "# Maritime-OCR-Bench\n\n\n## Overview\n\nMaritime-OCR-Bench is a comprehensive evaluation benchmark for assessing multimodal large model capabilities\non OCR-related tasks. The current released set contains 1,888 manually curated samples across five task types.\n\n## Task Types\n\n- **VQA**: Visual question answering on document/scene images\n- **IE**: Information extraction requiring strict JSON output\n- **parsing**: Text recognition and parsing from images\n- **json1**: Text spotting with JSON v1 structured output\n- **json2**: Text spotting with JSON v2 structured output\n\n## Evaluation Metrics\n\nEach task type uses a specialized scoring method:\n- VQA/parsing: Multi-dimensional text similarity (edit distance, char F1, LCS F1, table-aware similarity)\n- IE: Text coverage + JSON strictness (0.5 * coverage + 0.5 * json_strict)\n- json1/json2: DIoU layout score + text score (0.7 * diou + 0.3 * text)\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `maritime_ocr_bench` |\n| **Dataset ID** | [HiDolphin/MaritimeOCRBench](https://modelscope.cn/datasets/HiDolphin/MaritimeOCRBench/summary) |\n| **Paper** | N/A |\n| **Tags** | `MultiModal`, `QA` |\n| **Metrics** | `score` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 1,888 |\n| Prompt Length (Mean) | 102.91 chars |\n| Prompt Length (Min/Max) | 23 / 288 chars |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `IE` | 471 | 23 | 23 | 23 |\n| `VQA` | 471 | 39.65 | 28 | 141 |\n| `parsing` | 472 | 80 | 80 | 80 |\n| `json1` | 237 | 256.1 | 248 | 288 |\n| `json2` | 237 | 279.9 | 248 | 288 |\n\n**Image Statistics:**\n\n| Metric | Value |\n|--------|-------|\n| Total Images | 1,888 |\n| Images per Sample | min: 1, max: 1, mean: 1 |\n| Resolution Range | 108x50 - 4030x4075 |\n| Formats | jpeg, png |\n\n\n## Sample Example\n\n**Subset**: `IE`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"1d6ce119\",\n      \"content\": [\n        {\n          \"text\": \"请提取所有关键信息，并以 JSON 格式返回。\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: png, ~550.9KB]\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"{\\n  \\\"Document ID\\\": \\\"WiCE Error Message Description\\\",\\n  \\\"Revision\\\": \\\"REV 1\\\",\\n  \\\"Date\\\": \\\"2024-09-12\\\",\\n  \\\"Company_Logo_Text\\\":\\\"WIN GD\\\",\\n  \\\"Error Messages\\\": [\\n    {\\n      \\\"ID Number\\\": \\\"COFD-52\\\",\\n      \\\"Designation\\\": \\\"Fuel Pump Control Signal #2 Fa ... [TRUNCATED 1668 chars] ... nt must be within 4 ~ 20mA.\\\\n• If necessary, the sensor can be replaced with new one (Caution: before dismantling, do depressurize rail)\\\"\\n    }\\n  ],\\n  \\\"Footer\\\": \\\"T_PC-Drawing_Portrait | Release: 3.10 (2024-05-15)\\\",\\n  \\\"Page\\\": \\\"Page 20 of 83\\\"\\n}\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"IE\",\n  \"metadata\": {\n    \"task_type\": \"IE\",\n    \"prompt\": \"<image>请提取所有关键信息，并以 JSON 格式返回。\",\n    \"images\": [\n      \"images/580968569085497344_f33f02a81a.png\"\n    ]\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 maritime_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=['maritime_ocr_bench'],\n    dataset_args={\n        'maritime_ocr_bench': {\n            # subset_list: ['IE', 'VQA', 'parsing']  # 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": "# Maritime-OCR-Bench\n\n\n## 概述\n\nMaritime-OCR-Bench 是一个全面的评估基准，用于衡量多模态大模型在 OCR 相关任务上的能力。当前发布的数据集包含 1,888 个经过人工精心整理的样本，涵盖五种任务类型。\n\n## 任务类型\n\n- **VQA**：针对文档/场景图像的视觉问答\n- **IE**：要求严格 JSON 格式输出的信息抽取\n- **parsing**：从图像中进行文本识别与解析\n- **json1**：采用 JSON v1 结构化格式的文本检测\n- **json2**：采用 JSON v2 结构化格式的文本检测\n\n## 评估指标\n\n每种任务类型采用专门的评分方法：\n- VQA/parsing：多维文本相似度（编辑距离、字符级 F1、最长公共子序列 F1、表格感知相似度）\n- IE：文本覆盖率 + JSON 严格性（0.5 * coverage + 0.5 * json_strict）\n- json1/json2：DIoU 布局得分 + 文本得分（0.7 * diou + 0.3 * text）\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `maritime_ocr_bench` |\n| **数据集ID** | [HiDolphin/MaritimeOCRBench](https://modelscope.cn/datasets/HiDolphin/MaritimeOCRBench/summary) |\n| **论文** | N/A |\n| **标签** | `MultiModal`, `QA` |\n| **指标** | `score` |\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 | 1,888 |\n| 提示词长度（平均） | 102.91 字符 |\n| 提示词长度（最小/最大） | 23 / 288 字符 |\n\n**各子集统计数据：**\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| `IE` | 471 | 23 | 23 | 23 |\n| `VQA` | 471 | 39.65 | 28 | 141 |\n| `parsing` | 472 | 80 | 80 | 80 |\n| `json1` | 237 | 256.1 | 248 | 288 |\n| `json2` | 237 | 279.9 | 248 | 288 |\n\n**图像统计数据：**\n\n| 指标 | 值 |\n|--------|-------|\n| 图像总数 | 1,888 |\n| 每样本图像数 | 最小: 1, 最大: 1, 平均: 1 |\n| 分辨率范围 | 108x50 - 4030x4075 |\n| 格式 | jpeg, png |\n\n## 样例示例\n\n**子集**: `IE`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"1d6ce119\",\n      \"content\": [\n        {\n          \"text\": \"请提取所有关键信息，并以 JSON 格式返回。\"\n        },\n        {\n          \"image\": \"[BASE64_IMAGE: png, ~550.9KB]\"\n        }\n      ]\n    }\n  ],\n  \"target\": \"{\\n  \\\"Document ID\\\": \\\"WiCE Error Message Description\\\",\\n  \\\"Revision\\\": \\\"REV 1\\\",\\n  \\\"Date\\\": \\\"2024-09-12\\\",\\n  \\\"Company_Logo_Text\\\":\\\"WIN GD\\\",\\n  \\\"Error Messages\\\": [\\n    {\\n      \\\"ID Number\\\": \\\"COFD-52\\\",\\n      \\\"Designation\\\": \\\"Fuel Pump Control Signal #2 Fa ... [TRUNCATED 1668 chars] ... nt must be within 4 ~ 20mA.\\\\n• If necessary, the sensor can be replaced with new one (Caution: before dismantling, do depressurize rail)\\\"\\n    }\\n  ],\\n  \\\"Footer\\\": \\\"T_PC-Drawing_Portrait | Release: 3.10 (2024-05-15)\\\",\\n  \\\"Page\\\": \\\"Page 20 of 83\\\"\\n}\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"subset_key\": \"IE\",\n  \"metadata\": {\n    \"task_type\": \"IE\",\n    \"prompt\": \"<image>请提取所有关键信息，并以 JSON 格式返回。\",\n    \"images\": [\n      \"images/580968569085497344_f33f02a81a.png\"\n    ]\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 maritime_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=['maritime_ocr_bench'],\n    dataset_args={\n        'maritime_ocr_bench': {\n            # subset_list: ['IE', 'VQA', 'parsing']  # 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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