Image based Natural Scene Text Segmentation and Classification using Enhanced Retrieval and Optimization Technique

The enhancement of Optical Character Recognition (OCR) is a lively research domain for numerous well-known applications. These applications are automation of logistics, reading sign boards, helping visually impaired subjects, and retrieval of textual content from natural scene images. The foremost objective of OCR in these applications is to acquire significant performance in order to convert scene text into machine-readable text with adequate accuracy. Henceforth, an improved technique is proposed based on image processing for segmenting textual content (foreground) from unstructured and unconstrained backgrounds. Further, a multimodal feature fusion and selection is carried out for classification. These features are serially fused and out passed to Crow Search Optimization (CSO) in order to generate a salient feature vector using entropy as fitness function. After that, the optimal features are used by ensemble classifiers for classification. The presented work is comprehensively tested on three mainstream challenging datasets SVT, MSRA-TD500, and KAIST consisting several indoor/outdoor natural images with harsh scenic variabilities. The experimental results with existing benchmark techniques confirm that the presented technique performs better in retrieving textual content. Similarly, in the classification process, the proposed technique works fine on different experiments due to the novel optimization method. Finally, the presented optimization method is compared with its counterparts with the same setup and it is found that it also performs better.