Text Extraction from Images Using OCR
With the rapid growth of digital documents, extracting text from images has become an important task in many industries. Manually typing text from images is time-consuming and prone to errors. Optical Character Recognition (OCR) technology helps automate this process by converting images containing text into machine-readable text.
This project focuses on extracting text from images using OCR techniques. The system uses the Tesseract OCR engine along with Python libraries such as OpenCV and Pytesseract to detect and extract text from images. Image preprocessing techniques such as resizing, noise removal, blurring, and thresholding are applied to improve the accuracy of text recognition.
After preprocessing the image, the Tesseract library is used to extract the text. Further image processing techniques like erosion and contour detection are applied to identify characters and draw rectangles around detected words or patterns.
This project helps automate document analysis and reduces manual effort in typing text from images. It can be used in many real-world applications such as document digitization, automated data entry, license plate recognition, and information extraction from scanned documents.