PT Unknown
AU Juan Ignacio Toledo
   Sebastian Sudholt
   Alicia Fornes
   Jordi Cucurull
   A. Fink
   Josep Llados
TI Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling
BT Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)
PY 2016
BP 543
EP 552
VL 10029
DE Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection
AB The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches.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.
ER