Text extraction from natural scene images using Renyi entropy

Nowadays, development in machine vision incorporated with artificial intelligence surpasses the ability of human intelligence and its application expands exponentially with the increasing number of electronic gadgets in our day-to-day life. The explosive revolution in multimedia research leads to the need for expanding the utility of texts in a machine vision environment to promote web search operation. Hence, extracting text from images forms the core aspect of information retrieval-based intelligent system. This article is aimed towards extracting text from unconstrained environments. Here, the significance of the CIE-Lab colour space is analysed over text localisation assisted through Renyi entropy-based thresholding. The proposed algorithm is tested on the MSRA Text Detection 500 dataset (MSRA-TD500) and Street View Text (SVT) datasets, which are challenging datasets. Authors’ proposed Renyi entropy-based text localisation algorithm is successful in identifying blurred texts, texts with different font characteristics and multi-lingual texts with manifold orientations from complex background natural scenes.