Text extraction from natural scene images using Renyi entropy

The Journal of Engineering

  ^{1    1} 

[1] Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai, India;

关键词: natural scenes;    text detection;    entropy;    computer vision;    image colour analysis;    feature extraction;    image classification;    image retrieval;    Renyi entropy-based text localisation algorithm;    street view text datasets;    Web search operation;    artificial intelligence;    human intelligence;    natural scene images;    Text extraction;    complex background natural scenes;    multilingual texts;    blurred texts;    MSRA Text Detection 500 dataset;    Renyi entropy-based thresholding;    text localisation;    CIE-Lab colour space;    unconstrained environments;    information retrieval-based intelligent system;    machine vision environment;    multimedia research;    explosive revolution;    electronic gadgets;   

DOI  :  10.1049/joe.2018.5160

来源: publisher

【 摘 要 】

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.

【 授权许可】

CC BY