A Novel Learning-Free Word Spotting Approach Based on Graph Representation

Wang, Peng; Églin, Véronique; García, Christophe; Largeron, Christine; Lladós, Josep; Fornés, Alícia

doi:10.1109/das.2014.46

Cited by 34 publications

(22 citation statements)

References 13 publications

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“…Many authors from the document analysis field, understanding keyword spotting as being a particular case of the object recognition task, started to apply such keypoint matching techniques to the problem of keyword spotting [23,48,56,58]. Such matching techniques have been either used to directly estimate similarities between word images, or by searching the query model image within full pages in segmentation-free scenarios.…”

Section: Abstractpotting As An Object Recognition Taskmentioning

confidence: 98%

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

Aldavert

Rusiñol

Toledo

et al. 2015

IJDAR

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The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014.

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Section: Abstractpotting As An Object Recognition Taskmentioning

confidence: 98%

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

Aldavert

Rusiñol

Toledo

et al. 2015

IJDAR

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“…In contrast with [10] our approach results in a single graph per word. Hence, no additional assignment between graphs of different connected components is necessary during the matching process.…”

Section: Introductionmentioning

confidence: 94%

“…To the best of our knowledge only few graph-based KWS approaches have been proposed so far [9][10][11][12]. However, a graph-based representation is particularly interesting for KWS as graphs, in contrast with feature vectors, offer a more natural and comprehensive formalism for the representation of word images.…”

Section: Introductionmentioning

confidence: 99%

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Graph-Based Keyword Spotting in Historical Handwritten Documents

Stauffer

Fischer

Riesen

2016

Lecture Notes in Computer Science

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Abstract. The amount of handwritten documents that is digitally available is rapidly increasing. However, we observe a certain lack of accessibility to these documents especially with respect to searching and browsing. This paper aims at closing this gap by means of a novel method for keyword spotting in ancient handwritten documents. The proposed system relies on a keypoint-based graph representation for individual words. Keypoints are characteristic points in a word image that are represented by nodes, while edges are employed to represent strokes between two keypoints. The basic task of keyword spotting is then conducted by a recent approximation algorithm for graph edit distance. The novel framework for graph-based keyword spotting is tested on the George Washington dataset on which a state-of-the-art reference system is clearly outperformed.

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“…Recently, graphs have gained some attention in the field of handwritten document analysis [4] like for instance handwriting recognition [6], keyword spotting [7][8][9], or signature verification [10,11]. However, we still observe a lack of publicly available graph datasets that are based on handwritten word images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Graph Database for Handwritten Word Images

Stauffer

Fischer

Riesen

2016

Lecture Notes in Computer Science

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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.

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A Novel Learning-Free Word Spotting Approach Based on Graph Representation

Cited by 34 publications

References 13 publications

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

Graph-Based Keyword Spotting in Historical Handwritten Documents

A Novel Graph Database for Handwritten Word Images

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