Text/graphic separation using a sparse representation with multi-learned dictionaries

TitleText/graphic separation using a sparse representation with multi-learned dictionaries
Publication TypeConference Paper
Year of Publication2012
AuthorsDo, T-H, Tabbone, S, Ramos-Terrades, O
Conference Name21st International conference on pattern recognition
Conference LocationTsukuba, Japan
ISBN Number978-1-4673-2216-4

In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to create a final text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.