  Text Recognition is one of the challenging tasks of computer vision with
considerable practical interest. Optical character recognition (OCR) enables
different applications for automation. This project focuses on word detection
and recognition in natural images. In comparison to reading text in scanned
documents, the targeted problem is significantly more challenging. The use case
in focus facilitates the possibility to detect the text area in natural scenes
with greater accuracy because of the availability of images under constraints.
This is achieved using a camera mounted on a truck capturing likewise images
round-the-clock. The detected text area is then recognized using Tesseract OCR
engine. Even though it benefits low computational power requirements, the model
is limited to only specific use cases. This paper discusses a critical false
positive case scenario occurred while testing and elaborates the strategy used
to alleviate the problem. The project achieved a correct character recognition
rate of more than 80\%. This paper outlines the stages of development, the
major challenges and some of the interesting findings of the project.
