Optical Character Recognition (OCR) is a key technology that allows computers to extract readable text from images. It’s used to digitize printed documents so they can be edited, searched, and stored more compactly, and to automate data entry processes.
Imagine you’ve drawn a picture of your name on a piece of paper. Now imagine you’ve taught your computer to look at that picture and understand that it says your name. That’s basically what Optical Character Recognition is. It’s teaching computers to understand written or printed letters and words in pictures.
Optical Character Recognition (OCR) is a field of research in pattern recognition, artificial intelligence and computer vision. Its main aim is to design methods that can recognize and automatically translate different types of characters into machine-encoded text.
This process typically involves multiple stages. First, the system preprocesses the image to ensure the characters are clear and ready for recognition. This could involve noise removal, line detection, character segmentation and normalization, and other processes. Second, feature extraction is performed to identify characteristics of each character that might distinguish it from others. Finally, classification algorithms are used to match these features to known characters.
In modern applications, OCR has been revolutionized by the advent of deep learning and convolutional neural networks (CNNs). These algorithms can learn to recognize complex patterns across large datasets, making them highly useful for OCR. They can automatically learn thousands of unique features of each character, thus eliminating the need for manual feature extraction.
Applications of OCR include reading text from scanned documents, license plate recognition, automated data entry like processing cheques in banking, automation of receipt recognition in expense management, and assisting visually impaired individuals in text reading.
Two key metrics of OCR’s performance are accuracy and speed. Accuracy is affected by factors such as the print quality, font, and language of the text, shadows, glare, and distortions, and the algorithm’s ability to handle these complexities. Speed is essential in applications where large volumes of data are to be processed rapidly.
Despite significant advancements in the field, OCR still comes with its challenges. A major one is the recognition of handwritten text, which varies significantly from individual to individual and can be challenging to ascertain with high accuracy.
Artificial Intelligence, Machine Learning (ML),, Convolutional Neural Network (CNN),s, Pattern Recognition, Computer Vision, Image Preprocessing, Feature Extraction, Classification Algorithms, Deep Learning.