用OCR从发票中精准提取表格数据，提升财务自动化效率。
Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
- 结合Tesseract OCR与自定义后处理，动态优化图像并定位表格边界。
- 在非标准发票上实现高准确率的行列映射，提升结构化数据提取效果。
- 适合需要自动化票据处理的企业财务与数字归档系统。
本文提出一种基于OCR的发票表格提取流水线设计与实现。系统采用Tesseract OCR进行文本识别，并通过自定义后处理逻辑，实现对扫描发票文档中表格的检测、对齐与结构化数据提取。方法包含动态预处理、表格边界检测及行列表达映射，针对噪声大、格式不统一的发票进行了优化。所提出的流水线显著提升了数据提取的准确率与一致性，支持自动化财务流程和数字归档等真实应用场景。
原文摘要 · Abstract (English)
This paper presents the design and development of an OCR-powered pipeline for efficient table extraction from invoices. The system leverages Tesseract OCR for text recognition and custom post-processing logic to detect, align, and extract structured tabular data from scanned invoice documents. Our approach includes dynamic preprocessing, table boundary detection, and row-column mapping, optimized for noisy and non-standard invoice formats. The resulting pipeline significantly improves data extraction accuracy and consistency, supporting real-world use cases such as automated financial workflows and digital archiving.
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