--- name: chart-element-recognition-f8802e56fee0 description: 用于图表理解的图表元素识别原子 skill，适用于通用行业文档解析场景。 --- # 图表元素识别 Skill ## 数据来源 本 Skill 支持多种图表输入格式，核心数据来源包括： ### 1. 图表文件类型 - **图片格式**：PNG、JPG、JPEG、GIF、BMP等 - **PDF文档**：包含图表的PDF文档 - **Word文档**：Word文档中嵌入的图表 - **Excel文档**：Excel中的图表对象 ### 2. 图表类型 - **柱状图**：垂直柱状图、水平柱状图、分组柱状图 - **折线图**：单折线图、多折线图、面积图 - **饼图**：饼图、环形图、3D饼图 - **散点图**：散点图、气泡图 - **其他图表**：雷达图、热力图、树状图等 ### 3. 图表特征 - **图表质量**：清晰度、分辨率、色彩 - **图表复杂度**：简单图表、复杂图表、组合图表 - **图表语言**：中文图表、英文图表、多语言图表 - **图表风格**：标准风格、自定义风格 ### 4. 数据格式要求 - **文件路径**：本地文件路径或网络文件URL - **图片格式**：支持常见图片格式 - **文件权限**：需要读取权限 > 说明：本 Skill 不包含图表采集功能，需要用户提供图表文件。建议图表清晰且格式规范，以便进行准确的元素识别。 --- ## 功能 本 Skill 提供全面的图表元素识别能力，涵盖多种识别功能： ### 1. 图表类型识别 - **图表分类**：识别图表的类型和子类型 - **图表结构识别**：识别图表的整体结构 - **图表风格识别**：识别图表的风格和样式 - **组合图表识别**：识别组合图表中的各个子图表 ### 2. 坐标轴识别 - **坐标轴提取**：提取X轴和Y轴信息 - **坐标轴标签**：识别坐标轴的标签和单位 - **坐标轴刻度**：识别坐标轴的刻度和范围 - **坐标轴类型**：识别坐标轴类型（数值、时间、分类） ### 3. 数据系列识别 - **数据系列提取**：提取图表中的数据系列 - **数据点识别**：识别数据点的位置和数值 - **数据标签识别**：识别数据标签和注释 - **图例识别**：识别图例信息和映射关系 ### 4. 文本信息提取 - **标题提取**：提取图表标题和副标题 - **标签提取**：提取各类标签和说明文字 - **注释提取**：提取图表中的注释和说明 - **单位提取**：提取数值单位和量纲 ### 5. 数据值提取 - **数值提取**：从图表中提取数值数据 - **数据重建**：重建图表的原始数据 - **数据验证**：验证提取数据的准确性 - **数据格式化**：格式化提取的数据 ### 6. 高级处理功能 - **图表理解**：理解图表的含义和表达的信息 - **趋势分析**：分析图表中的趋势和模式 - **异常检测**：检测图表中的异常数据点 - **图表描述生成**：生成图表的文字描述 --- ## 使用示例 ### 输出示例 ```json { "chart_info": { "filename": "sales_chart.png", "file_size": 256000, "format": "png", "dimensions": [800, 600] }, "chart_type": { "primary_type": "bar_chart", "sub_type": "grouped_bar_chart", "confidence": 0.95 }, "axes": { "x_axis": { "label": "季度", "type": "categorical", "categories": ["Q1", "Q2", "Q3", "Q4"], "unit": null }, "y_axis": { "label": "销售额", "type": "numerical", "range": [0, 1000], "unit": "万元" } }, "data_series": [ { "series_name": "产品A", "series_type": "bar", "data_points": [ {"x": "Q1", "y": 200}, {"x": "Q2", "y": 250}, {"x": "Q3", "y": 300}, {"x": "Q4", "y": 280} ], "color": "#FF5733" }, { "series_name": "产品B", "series_type": "bar", "data_points": [ {"x": "Q1", "y": 150}, {"x": "Q2", "y": 180}, {"x": "Q3", "y": 220}, {"x": "Q4", "y": 250} ], "color": "#33C3F0" } ], "text_elements": { "title": "2024年季度销售额对比", "subtitle": null, "legend": ["产品A", "产品B"], "annotations": [] }, "extracted_data": { "raw_data": [ {"季度": "Q1", "产品A": 200, "产品B": 150}, {"季度": "Q2", "产品A": 250, "产品B": 180}, {"季度": "Q3", "产品A": 300, "产品B": 220}, {"季度": "Q4", "产品A": 280, "产品B": 250} ], "data_format": "table" }, "analysis": { "trend": "increasing", "key_insights": ["产品A销售额持续增长", "Q3达到峰值"], "description": "该图表展示了2024年四个季度产品A和产品B的销售额对比情况，整体呈现上升趋势。" } } ``` --- ## 注意事项与限制 ### 1. 图表质量要求 - 清晰图表识别准确率较高 - 模糊或低分辨率图表可能影响识别 - 手绘图表识别准确率较低 ### 2. 图表类型支持 - 标准图表类型识别准确率较高 - 非标准或自定义图表可能有限 - 复杂组合图表需要特殊处理 ### 3. 数据提取准确性 - 清晰标注的数值提取准确率较高 - 需要估算的数值可能不够准确 - 3D图表数据提取可能有限 ### 4. 语言和文字 - 中文图表识别准确率较高 - 英文图表需要相应语言支持 - 特殊字体可能影响文字识别 ### 5. 使用限制 - 本 Skill 不包含图表编辑功能 - 识别结果需要人工复核 - 复杂图表可能需要人工分析 --- ## 参考资料 - 见 references/ 目录中的相关文档，包括： - 图表元素识别方法手册 - OCR识别算法说明 - 图表类型分类指南 - 性能优化指南 --- Source attribution: aifinlab Original project: https://github.com/aifinlab/FinClaw/blob/9e62862d1412157fa03ffd1f0b74969dcf0ed66c/skills/archive/chart-element-recognition/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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