Text region extraction and text segmentation on camera-captured document style images

Citations Over Time

Abstract

In this paper, we propose a text extraction method from camera-captured document style images and propose a text segmentation method based on a color clustering method. The proposed extraction method detects text regions from the images using two low-level image features and verifies the regions through a high-level text stroke feature. The two level features are combined hierarchically. The low-level features are intensity variation and color variance. And, we use text strokes as a high-level feature using multi-resolution wavelet transforms on local image areas. The stroke feature vector is an input to a SVM (support vector machine) for verification, when needed. The proposed text segmentation method uses color clustering to the extracted text regions. We improved K-means clustering method and it selects K and initial seed values automatically. We tested the proposed methods with various document style images captured by three different cameras. We confirmed that the extraction rates are good enough to be used in real-life applications.

Related Papers

• → Mean Shift Algorithm and its Application in Tracking of Objects(2006)29 cited
• → Object tracking by applying mean-shift algorithm into particle filtering(2009)7 cited
• Application of Mean shift method in target tracking(2007)
• → Algorithm of Adaptive Kernel-Bandwidth for Mean-Shift Based on Boundary Force(2010)2 cited
• Application of Non-Parametric Mean Shift Method to Computer Vision(2010)