Abstract
In this paper, a region-based text localization that is robust for multiple languages is presented. Maximally Stable Extremal Regions (MSERs) are used for detecting candidates of text areas. The MSER components are grouped based on their connectivity in a feature space by using a new proposed rule for assigning the connectivity. The groups of components are classified into three classes that are text regions with high confidence, text region with low confidence, and non-text regions. A chain of text attribute constraint decision with the double-threshold scheme is developed to identify text regions. A sequence of constraint decision is designed to minimize the complexity based on short-circuit evaluation of logic operators. The regions that satisfy all strong constraints will be considered as text regions with high confidence while the regions that fail in some strong constraints but satisfy all weak constraints will be considered as text regions with low confidence. The final text regions are obtained from all text regions with high confidence and text regions with low confidence that have connectivity to text regions with high confidence. The proposed scheme is evaluated by using the natural scene images that consist of totally nine languages with different text alignments and camera views. The experiment shows that our proposed scheme can provide the satisfy results in comparison with baseline method.
Highlights
Text localization is a process to detect text areas in images
Fifty natural scene images composed of many languages such as English, Chinese, Japanese, Korean, Arabic, and Thai with various text alignments and camera views are collected and used for testing the performance of the proposed scheme
The Maximally Stable Extremal Regions (MSERs) based text localization using double-threshold scheme was presented in this paper
Summary
The text localization can be applied in many applications such as blind assistive systems [1], scene categorization [2], robot navigation [3], and content-based retrieval [4]. X. Liu, et al [3] proposed an algorithm to detect text based landmarks such as nameplate and information sign for navigation robot under indoor environment. Edge density and variation of orientation are used as features, and morphological operators and some heuristic constraints are applied for clustering and filtering text regions in the scene. The other example of text localization application is a content based video retrieval system. H. Yang, et al [4] proposed an automated system to indicate and search lecture videos based on video contents within large lecture video archives by analyzing speech and text information
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