g-DICE: graph mining-based document information content exploitation

In this paper, we present document information content (i.e. text fields) extraction technique via graph mining. Real-world users first provide a set of key text fields from the document image which they think are important. These fields are used to initialise a graph where nodes are labelled with the field names in addition to other features such as size, type and number of words, and edges are attributed with relative positioning between them. Such an attributed relational graph is then used to mine similar graphs from document images which are used to update the initial graph iteratively each time we extract them, to produce a graph model. Graph models, therefore, are employed in the absence of users. We have validated the proposed technique and evaluated its scientific impact on real-world industrial problem with the performance of 86.64 % precision and 90.80 % recall by considering all zones, viz. header, body and footer. More specifically, the proposed technique is well suited for table processing (i.e. extracting repeated patterns from the table) and it outperforms the state-of-the-art method by approximately more than 3 %.