An Efficient and Robust Method for Text Detection in Low and High Resolution Natural Scene Images

Abstract

In this paper, we propose efficient and sturdy technique for investigating texts in natural scene footage. A fast and effective pruning formula is designed to extract Maximally Stable External Regions (MSERs) as character candidate’s victimization the strategy of minimizing regularized variations. Character candidates form into text candidates by the single-link clump formula, wherever distance weights and clump threshold unit of measurement learned by a completely distinctive self-training distance metric learning formula. The probabilities of text candidates like non-text unit of measurement estimable with a temperament classifier. Text candidates with high non-text probabilities unit of density eliminated and texts unit of measurement acknowledged with a document classifier. Text find in natural scene footage is also an important for several content-based image resolve. Experiments on polyglot, street browse; multi-direction and even born-digital databases conjointly demonstrate the effectiveness of the reposed technique.
DOI: 10.17762/ijritcc2321-8169.150782