Abstract
This paper proposes a simple yet effective method for anomaly detection in natural scene images improving natural scene text detection and recognition. In the last decade, there has been significant progress towards text detection and recognition in natural scene images. However, in cases where there are logos, company symbols, or other decorative elements for text, existing methods do not perform well. This work considers such misclassified components, which are part of the text as anomalies, and presents a new idea for detecting such anomalies in the text for improving text detection and recognition in natural scene images. The proposed method considers the result of the existing text detection method as input for segmenting characters or components based on saliency map and rough set theory. For each segmented component, the proposed method extracts feature from the saliency map based on density, pixel distribution, and phase congruency to classify text and non-text components by exploring a fuzzy-based classifier. To verify the effectiveness of the method, we have performed experiments on several benchmark datasets of natural scene text detection, namely, MSRATD-500 and SVT. Experimental results show the efficacy of the proposed method over the existing ones for text detection and recognition in these datasets.
Highlights
Researchers have focused on scene text detection and recognition using deep learning [1], [2], beating the previous state-of-the-art methods and becoming practically useful
We propose to obtain saliency for the text, which enhances the fine details in the images [18]–[20]
Aside from the complicated background, the photographs are acquired from an oblique perspective, which affects the quality of the images
Summary
Researchers have focused on scene text detection and recognition using deep learning [1], [2], beating the previous state-of-the-art methods and becoming practically useful When it comes to scene text associated with symbols, logos, or non-text components that share text properties, the performance of such methods degrade [1], [2]. Since these situations are pretty common in real natural scenes, this work focuses on the improvement in these areas. This results in a good performance in text detection and recognition as well
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