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

Citations Over TimeTop 10% of 2004 papers

Abstract

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.

Related Papers

• → Feature Extraction Using Wavelet-PCA and Neural Network for Application of Object Classification & Face Recognition(2010)29 cited
• → ICA and PCA integrated feature extraction for classification(2016)32 cited
• → Partial discharge pattern recognition using multiscale feature extraction and support vector machine(2013)11 cited
• Image feature extraction from wavelet scalogram based on kernel principle component analysis(2009)
• → Feature Bias Correction: A Feature Augmentation Method for Long-tailed Recognition(2023)