Real-time text detection is a critical component in text extraction and Natural Language Processing (NLP), challenged by various formats, fonts, colors, sizes, orientations, and languages amidst complex backgrounds. Recent advances in deep learning have enhanced natural scene text identification, and tools like Tesseract OCR and OpenCV have become pivotal in addressing these challenges. These open-source tools facilitate real-time text detection and processing, leveraging techniques such as binarization, de-skewing, and character segmentation to improve accuracy. Tesseract utilizes methods like word finding and neural networks to detect text effectively, while OpenCV supports real-time scene detection through extensive in-built algorithms. Preprocessing techniques and OCR algorithms play essential roles in enhancing text detection and recognition in various applications, from robotics to image retrieval. The integration of real-time OCR capabilities into systems, including mobile scanning apps, allows for diverse applications and customization, such as white-listing and black-listing characters, to tailor text detection for specific needs.