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In this paper, we propose a framework for isolating text regions from natural scene images. The main algorithm has two functions: it generates text region candidates, and it verifies of the label of the candidates (text or non-text). The text region candidates are generated through a modified K-means clustering algorithm, which references texture features, edge information and color information. The...
Text separation in natural scenes is a crucial step to recognize scene text. Since computational power in a mobile device is limited, current text extraction methods are impractical in real-time devices. We propose efficient text extraction methods by utilizing user's indication. When user simply indicates focus or draws the line on touch screen, the system can extract text in natural scenes efficiently...
This paper presents a rule-based approach that utilizes some types of contextual information to improve the accuracy of handwritten mathematical expression(ME) recognition. Mining context from corpus is not practical for ME recognition due to the complexity originated from 2-D nature of MEs. For practicality, we identify typical types of consistencies that are often found in customary usage and general...
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