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

Abstract

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.