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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.