A Novel Graph Database for Handwritten Word Images
For several decades graphs act as a powerful and flexible representation formalism in pattern recognition and related fields. For instance, graphs have been employed for specific tasks in image and video analysis, bioinformatics, or network analysis. Yet,
- PDF / 332,129 Bytes
- 11 Pages / 439.37 x 666.142 pts Page_size
- 75 Downloads / 182 Views
Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstr. 16, 4600 Olten, Switzerland {michael.stauffer,kaspar.riesen}@fhnw.ch 2 University of Fribourg and HES-SO, 1700 Fribourg, Switzerland [email protected] 3 Department of Informatics, University of Pretoria, Pretoria, South Africa Abstract. For several decades graphs act as a powerful and flexible representation formalism in pattern recognition and related fields. For instance, graphs have been employed for specific tasks in image and video analysis, bioinformatics, or network analysis. Yet, graphs are only rarely used when it comes to handwriting recognition. One possible reason for this observation might be the increased complexity of many algorithmic procedures that take graphs, rather than feature vectors, as their input. However, with the rise of efficient graph kernels and fast approximative graph matching algorithms, graph-based handwriting representation could become a versatile alternative to traditional methods. This paper aims at making a seminal step towards promoting graphs in the field of handwriting recognition. In particular, we introduce a set of six different graph formalisms that can be employed to represent handwritten word images. The different graph representations for words, are analysed in a classification experiment (using a distance based classifier). The results of this word classifier provide a benchmark for further investigations.
Keywords: Graph benchmarking dataset representation for handwritten words
1
·
Graph repository
·
Graph
Introduction
Structural pattern recognition is based on sophisticated data structures for pattern representation such as strings, trees, or graphs1 . Graphs are, in contrast with feature vectors, flexible enough to adapt their size to the complexity of individual patterns. Furthermore, graphs are capable to represent structural relationships that might exist between subparts of the underlying pattern (by means of edges). These two benefits turn graphs into a powerful and flexible representation formalism, which is actually used in diverse fields [1,2]. The computation of a dissimilarity between pairs of graphs, termed graph matching, is a basic requirement for pattern recognition. In the last four decades 1
Strings and trees can be seen as special cases of graphs.
c Springer International Publishing AG 2016 A. Robles-Kelly et al. (Eds.): S+SSPR 2016, LNCS 10029, pp. 553–563, 2016. DOI: 10.1007/978-3-319-49055-7 49
554
M. Stauffer et al.
quite an arsenal of algorithms has been proposed for the task of graph matching [1,2]. Moreover, also different benchmarking datasets for graph-based pattern recognition have been made available such as ARG [3], IAM [4], or ILPIso [5]. These dataset repositories consist of synthetically generated graphs as well as graphs that represent real world objects. Recently, graphs have gained some attention in the field of handwritten document analysis [4] like for instance handwriting recognition [6], keyword spotting [7–
Data Loading...
