Multi-script text versus non-text classification of regions in scene images

Sriman, B. & Schomaker, L., 20-Apr-2019, (Accepted/In press) In : Journal of Visual Communication and Image Representation.

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  • Multi-script text versus non-text classification of regions in scene images

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Text versus non-text region classification is an essential but difficult step in scene-image analysis due to the considerable shape complexity of text and background patterns. There exists a high probability of confusion between background elements and letter parts. This paper proposes a feature-based classification of image blocks using the color autocorrelation histogram (CAH) and the scale-invariant feature transform (SIFT) algorithm, yielding a combined scale and color-invariant feature suitable for scene-text classification. For the evaluation, features were extracted from different color spaces, applying color-histogram autocorrelation. The color features are adjoined with a SIFT descriptor. Parameter tuning is performed and evaluated. For the classification, a standard nearest-neighbor (1NN) and a support-vector machine (SVM) were compared. The proposed method appears to perform robustly and is especially suitable for Asian scripts such as Kannada and Thai, where urban scene-text fonts are characterized by a high curvature and salient color variations.

Original languageEnglish
JournalJournal of Visual Communication and Image Representation
Publication statusAccepted/In press - 20-Apr-2019


  • Text detection in scene images, Text/non-text classification, Color features, Color histogram autocorrelation

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