Multi-script text versus non-text classification of regions in scene imagesSriman, B. & Schomaker, L., 20-Apr-2019, (Accepted/In press) In : Journal of Visual Communication and Image Representation.
Research output: Contribution to journal › Article › Academic › peer-review
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.
|Journal||Journal of Visual Communication and Image Representation|
|Publication status||Accepted/In press - 20-Apr-2019|
- Text detection in scene images, Text/non-text classification, Color features, Color histogram autocorrelation