Title :
Segmentation-free pattern spotting in historical document images
Author :
Sovann En;Caroline Petitjean;Stephane Nicolas;Laurent Heutte
Author_Institution :
LITIS, University of Rouen, Saint Etienne du Rouvray, 76800, France
Abstract :
Pattern spotting consists of retrieving the most similar graphical patterns from a collection of document images. Inspired by the recent advances in computer vision and word spotting techniques, we propose in this paper an unsupervised, segmentation-free pattern spotting system. Overall, the system includes a powerful patch-based framework, the bag of visual word model with an offline sliding window mechanism to avoid heavy computational burden during the retrieval process. Our system takes advantage of the most recent powerful compression and distance approximation techniques (product quantization and asymmetric distance computation) to efficiently index the great number of sub-windows produced by sliding windows and allows to retrieve small sized queries in a large indexed corpus.
Keywords :
"Image segmentation","Computer vision","Lead"
Conference_Titel :
Document Analysis and Recognition (ICDAR), 2015 13th International Conference on
DOI :
10.1109/ICDAR.2015.7333833