%0 Conference Proceedings
%T Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling
%A Juan Ignacio Toledo
%A Sebastian Sudholt
%A Alicia Fornes
%A Jordi Cucurull
%A A. Fink
%A Josep Llados
%B Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)
%D 2016
%V 10029
%I Springer International Publishing
%@ 978-3-319-49054-0
%F Juan Ignacio Toledo2016
%O DAG; 600.097; 602.006
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2877), last updated on Tue, 01 Feb 2022 12:55:03 +0100
%X The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results.
%K Document image analysis
%K Word image categorization
%K Convolutional neural networks
%K Named entity detection
%U http://link.springer.com/chapter/10.1007/978-3-319-49055-7_48
%U http://refbase.cvc.uab.es/files/TSF2016.pdf
%P 543-552