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Text Recognition is one of the challenging tasks of computer vision withconsiderable practical interest. Optical character recognition (OCR) enablesdifferent applications for automation. This project focuses on word detectionand recognition in natural images. In comparison to reading text in scanneddocuments, the targeted problem is significantly more challenging. The use casein focus facilitates the possibility to detect the text area in natural sceneswith greater accuracy because of the availability of images under constraints.This is achieved using a camera mounted on a truck capturing likewise imagesround-the-clock. The detected text area is then recognized using Tesseract OCRengine. Even though it benefits low computational power requirements, the modelis limited to only specific use cases. This paper discusses a critical falsepositive case scenario occurred while testing and elaborates the strategy usedto alleviate the problem. The project achieved a correct character recognitionrate of more than 80\%. This paper outlines the stages of development, themajor challenges and some of the interesting findings of the project.

Image Processing Based Scene-Text Detection and Recognition with Tesseract