Abstract
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.
Abstract (translated)
本文介绍了一种基于OCR的管道设计与开发，用于从发票中高效提取表格信息。该系统利用Tesseract OCR进行文本识别，并结合自定义后处理逻辑来检测、对齐并从扫描的发票文档中抽取结构化的表格数据。我们的方法包括动态预处理、表格边界检测和行列映射，特别针对嘈杂且非标准的发票格式进行了优化。所生成的管道显著提高了数据提取的准确性和一致性，支持诸如自动化财务工作流程和数字存档等实际应用场景。
URL
https://arxiv.org/abs/2507.07029
