Improving Classification of an Industrial Document Image Database by Combining Visual and Textual Features
2014 11th IAPR International Workshop on Document Analysis Systems (DAS) (2014)
April 7, 2014 to April 10, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DAS.2014.44
The main contribution of this paper is a new method for classifying document images by combining textual features extracted with the Bag of Words (BoW) technique and visual features extracted with the Bag of Visual Words (BoVW) technique. The BoVW is widely used within the computer vision community for scene classification or object recognition but few applications for the classification of entire document images have been submitted. While previous attempts have been showing disappointing results by combining visual and textual features with the Borda-count technique, we're proposing here a combination through learning approach. Experiments conducted on a 1925 document image industrial database reveal that this fusion scheme significantly improves the classification performances. Our concluding contribution deals with the choosing and tuning of the BoW and/or BoVW techniques in an industrial context.
Visualization, Optical character recognition software, Databases, Context, Support vector machines, Feature extraction, Layout
O. Augereau, N. Journet, A. Vialard and J. Domenger, "Improving Classification of an Industrial Document Image Database by Combining Visual and Textual Features," 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), Tours, France, 2014, pp. 314-318.