Abstract
In handwritten text recognition, “sliding window” feature extraction represent the visual information contained in written text as feature vector sequences. In this paper, we explore the parameter space of feature weights in search for optimal weights and feature selection using the coordinate descent method. We report a gain of about 5% AUC performance. We use a public dataset for evaluation and also discuss the effects and limitations of “word pruning,” a technique in word spotting that is commonly used to boost performance and save computational time.
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Wahlberg, F., Brun, A. (2013). Feature Weight Optimization and Pruning in Historical Text Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_10
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DOI: https://doi.org/10.1007/978-3-642-41939-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
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