© 2014, Springer-Verlag Berlin Heidelberg. In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
|Journal||International Journal on Document Analysis and Recognition|
|Publication status||Published - 1 Jan 2014|
- Digital mail room
- Multimodal page classification
- Visual and textual document description