Abstract: Image recognition and optical character recognition technologies have become an integral part of our daily lives due to increasing computing power and the proliferation of scanning devices. A printed document can be quickly converted to a digital text file using optical character recognition and edited by the user. The time required to digitize documents is therefore minimal. This is especially useful when archiving large print volumes. In this study, we show how image processing techniques can be used in combination with optical character recognition to improve recognition accuracy and improve efficiency in extracting text from images. Two of his software systems are developed and tested in this study: a character recognition system applied to cosmetics-related advertising images and a recognition and text recognition system for natural scenes. Experimental results show that the proposed system can accurately recognize text in images. Keywords: Image processing, Text detection, Visiting card, Optical character recognition (OCR),Named entity recognition (NER),Computer vision, OpenCV, Pytesseract, Tesseract OCR engine, Grayscale conversion, Noise removal, Thresholding, Named entities, Business name extraction, Owner name extraction, Address extraction, Contact number extraction, Email address extraction, Website extraction, Data categorization, Information extraction, Data organization, Spreadsheet integration, Accuracy enhancement, Efficiency improvement, Error reduction, Python programming, Data processing, Data extraction, Text extraction