Title:	A Handcrafted Feature Descriptor for Word Recognition using Embedded Prototype Subspace Classifiers
Authors:	Hast, Anders
Citation:	Journal of WSCG. 2022, vol. 30, no. 1-2, p. 82-90.
Issue Date:	2022
Publisher:	Václav Skala - UNION Agency
Document type:	článek article
URI:	http://hdl.handle.net/11025/49397
ISSN:	1213-6972 (print) 1213-6964 (on-line)
Keywords:	diskrétní Fourierova transformace;Gaborovy filtry;podprostory;vestavěné prototypy;shlukování;F-skóre;variabilita;hluboké učení;t-SNE
Keywords in different language:	discrete Fourier transform;Gabor filters;subspaces;embedded prototypes;clustering;F1 score;variability;deep learning;t-SNE
Abstract in different language:	The purpose of this paper is to in detail describe and analyse a Fourier based handcrafted descriptor for word recognition. Especially, it is discussed how the Variability in the results can be analysed and visualised. This efficiency of the descriptor is evaluated for the use with embedded prototype subspace classifiers for handwritten word recognition. Nonetheless, it can be used with any classifier for any purpose. An hierarchical composition of discrete semicircles in the Fourier-space is proposed and it will will be show how this compares to Gabor filters, which can be used to extract edges in an image. In comparison to Histogram of Oriented Gradients, the proposed feature descriptor performs better in this scenario. Compression using PCA turns out to be able to increase both the F1-score as well as decreasing the Variability.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	Volume 30, Number 1-2 (2021)

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.