Scene text extraction in natural scene images using hierarchical feature combining and verification

• Main Authors: Kim, K.C., Byun, H.R., Song, Y.J., Choi, Y.W., Chi, S.Y., Kim, K.K., Chung, Y.K.
• Format: Conference Proceeding
• Language: English
• Subjects: Color; Computer science; Data mining; Graphics; Image recognition; Layout; Roads; Testing; Text recognition; Wavelet transforms
• ISSN: 1051-4651; 2831-7475
• DOI: 10.1109/ICPR.2004.1334350

We propose a method that extracts text regions in natural scene images using low-level image features and that verifies the extracted regions through a high-level text stroke feature. Then the two level features are combined hierarchically. The low-level features are color continuity, gray-level variation and color variance. The color continuity is used since most of the characters in a text region have the same color, and the gray-level variation is used since the text strokes are distinctive to the background in their gray-level values. Also, the color variance is used since the text strokes are distinctive in their colors to the background, and this value is more sensitive than the gray-level variations. As a high level feature, text stroke is examined using multi-resolution wavelet transforms on local image areas and the feature vector is input to a SVM (support vector machine) for verification. We tested the proposed method with various kinds of the natural scene images and confirmed that extraction rates are high even in complex images.