While storing invoice content as metadata to avoid paper document processing
may be the future trend, almost all of daily issued invoices are still printed
on paper or generated in digital formats such as PDFs. In this paper, we
introduce the OCRMiner system for information extraction from scanned document
images which is based on text analysis techniques in combination with layout
features to extract indexing metadata of (semi-)structured documents. The
system is designed to process the document in a similar way a human reader
uses, i.e. to employ different layout and text attributes in a coordinated
decision. The system consists of a set of interconnected modules that start
with (possibly erroneous) character-based output from a standard OCR system and
allow to apply different techniques and to expand the extracted knowledge at
each step. Using an open source OCR, the system is able to recover the invoice
data in 90% for English and in 88% for the Czech set.

OCRMiner 系统使用文本分析技术与布局特征相结合的方法从扫描文档图像中提取 (半) 结构化文档的索引元数据。该系统由多个相互连接的模块组成，使用开源 OCR 系统，其英文数据召回率达 90％，捷克语数据召回率为 88％。