An Image to Text converter using OCR (Optical Character Recognition) works by analyzing an image to identify and extract text. It starts with preprocessing, which includes binarizing the image, removing noise, and aligning text for better accuracy.
The system then segments the image into regions, such as lines or individual characters, and applies feature extraction techniques to identify text patterns. Modern OCR systems use deep learning models like CNNs or LSTMs to recognize characters and words more accurately.
After recognition, postprocessing is performed to correct errors and format the extracted text. Applications include digitizing documents, translating text from images, and automating form processing in various industries.