Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/597
Title: Handwritten word preprocessing for database adaptation
Authors: Oprean, Cristina
Likforman-Sulem, Laurence
Mokbel, Chafic 
Affiliations: Department of Electrical Engineering 
Keywords: Handwritten word recognition
Database Adaptation
Word preprocessing
Issue Date: 2013
Part of: Proceedings of SPIE
Volume: 8658
Start page: 1
End page: 10
Conference: Document Recognition and Retrieval Conference (20th : 5-7 Feb 2013 : San Francisco) 
Abstract: 
Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level, . . .). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.
URI: https://scholarhub.balamand.edu.lb/handle/uob/597
Open URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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