Detection of text in camera-based images is a vital requirement for several computer vision applications. Text detection task is frequently challenging due to difficulties like composite backgrounds, dissimilarities of text orientations, font, size, color. The aim is to recognize text in a combine manner by searching for words from the image into text areas or single character candidates. Text captured in natural scenes is most of the times with multiple orientations and point of distortions. Currently most research efforts focuses on horizontal orientation from images. To address same issues a novel approach unified distance metric learning framework is proposed an adaptive hierarchical clustering, which learns weights of the character candidates once at a time and adaptively integrate different feature similarities. An effective multi-orientation text detection system, which constructs the text character candidates by grouping characters based on an adaptive clustering.

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Distance Metric Learning Multi-orientation Scene Text Detection