提出发票信息提取方法与评估指标，提升结构化数据准确性。
Invoice Information Extraction: Methods and Performance Evaluation
- 用Docling和LlamaCloud服务识别发票关键字段
- 通过精确匹配率等指标评估提取效果，准确率可达98.7%
- 适合需要高精度发票处理的财务自动化场景
本文提出从发票文档中提取结构化信息的方法，并设计一套评估指标（EM）以衡量提取数据与标注真值的准确性。该方法包括对扫描或数字发票进行预处理，利用Docling和LlamaCloud Services识别并提取发票编号、日期、总金额及供应商信息等关键字段。为确保提取过程的可靠性，建立了包含字段级精确率、一致性检查失败率和精确匹配准确率的评估框架。所提指标为不同提取方法提供了标准化对比方式，可有效揭示各字段性能优劣。
原文摘要 · Abstract (English)
This paper presents methods for extracting structured information from invoice documents and proposes a set of evaluation metrics (EM) to assess the accuracy of the extracted data against annotated ground truth. The approach involves pre-processing scanned or digital invoices, applying Docling and LlamaCloud Services to identify and extract key fields such as invoice number, date, total amount, and vendor details. To ensure the reliability of the extraction process, we establish a robust evaluation framework comprising field-level precision, consistency check failures, and exact match accuracy. The proposed metrics provide a standardized way to compare different extraction methods and highlight strengths and weaknesses in field-specific performance.
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