Handwriting Similarities as Features for the Characterization of Writer’s Style Invariants and Image Compression

Gaceb, Djamel; Églin, Véronique; Bres, Stéphane; Emptoz, Hubert

doi:10.1007/11867661_70

Cited by 2 publications

(1 citation statement)

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“…Additionally, by inserting x-line, baseline to extract upper/lower profiles features or by using the background-to-ink transition features (Rath, 2003). There are many others ways for features description: blob descriptors (Lowe, 2004) (Bay, 2006), structure or shape descriptors as Fourier transform (Jawahar, 2004), Gabor features (Gaceb, 2006), skeletons based features (Lladós,2007),HistogramofgradientFeatures(HOG) (Terasawa,2009),moments: Humoments(Zhihu,2010),Zernikemoments (Rao,2013),texturedescriptors (Dey, 2016),Radialmoments (Kassis,2016),etc.Othershavecombineddifferentfeatures andusedamultiscalerepresentationforwordimagesas (Mhiri,2017).…”

Section: Related Workmentioning

confidence: 99%

Word Spotting Based on Bispace Similarity for Visual Information Retrieval in Handwritten Document Images

Benabdelaziz

Gaceb

Haddad

2019

International Journal of Computer Vision and Image Processing

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Retrieving information from a huge collection of ancient handwritten documents is important for indexing, interpreting, browsing, and searching documents in various domains. Word spotting approaches are widely used in this context but have several limitations related to the complex properties of handwriting. These can appear at several steps: interest point detection, description, and matching. This article proposes a new word spotting approach for the word retrieval in handwritten document, which mainly leverages the properties of image gradients for visual features detection and description. The proposed approach is based on the combination of spatial relationships with textural information to design a more accurate matching. The experimental results of the proposed approach demonstrate a higher performance over the Jeremy Bentham dataset, evaluated following the recent benchmarks of ICDAR 2015 Competition on Keyword Spotting for Handwritten Documents.

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