Textual Information Extraction in Document Images Guided by a Concept Lattice

Text Information Extraction in images is concerned with extracting the relevant text data from a collection of document images. It consists in localizing (determining the location) and recognizing (transforming into plain text) text contained in document images. In this work we present a textual information extraction model consisting in a set of prototype regions along with pathways for browsing through these prototype regions. The proposed model is constructed in four steps: (1) produce synthetic invoice data containing the textual information of interest, along with their spatial positions; (2) partition the produced data; (3) derive the prototype regions from the obtained partition clusters; (4) build the concept lattice of a formal context derived from the prototype regions. Experimental results, on a corpus of 1000 real-world scanned invoices show that the proposed model improves significantly the extraction rate of an Optical Character Recognition (OCR) engine.