Text Detection And Extraction Using OpenCV and OCR

This is an image text detection and extraction project with OpenCV and Optical Character Recognition (OCR) methods. Preprocessing of images is performed with OpenCV, such as grayscale, noise removal, resizing, and thresholding, to improve the quality of images. Tesseract OCR is utilized to detect and extract text and convert it into a machine-readable text. The system is also able to automatically identify Regions of Interest (ROIs) in which text is likely to reside, thus streamlining text recognition. It renders it appropriate for document digitization, text extraction from signs or license plates, and text searching within images. By automating document processing and data entry, the system minimizes manual work, raising efficiency and accuracy. It also minimizes accessibility barriers for the blind by transforming written content into accessible formats. Companies can utilize it for invoices and receipts processing, researchers for reading from historic documents, and AI programs for automated text recognition. The project is scalable since it can be able to deal with various categories of images and real-world applications. The project is applicable to data mining, archiving, and use cases where AI employs fast and effective text retrieval. It is cost-effective and effective when utilized for the processing of large volumes of text through the combination of OpenCV and OCR. Because of its capability to handle intricate images, the system is applicable in fields of automation, accessibility, and improving productivity in different fields. Keywords: Text Detection, Optical Character Recognition (OCR), OpenCV, Image Processing, Feature Extraction, Tesseract, Machine Learning, Deep Learning, Preprocessing, Computer Vision.