Optical text recognition (OCR) using artificial intelligence (AI) has become an important tool for converting scans of documents, images and handwritten text into editable text files. This allows for significant improvements in information processing and accessibility. The OCR process, which is based on machine learning algorithms, transforms an image containing text into a format that can be edited, searched, and analyzed.
Modern AI technologies have greatly improved the accuracy of OCR. Using deep neural networks such as Convolutional Neural Networks (CNNs), AI is able to not only recognize standard printed text, but also account for different fonts, styles, and even handwritten text. The process begins by breaking down an image into individual elements, and the AI analyzes each character, comparing it to a trained model to determine exactly what the character is. Importantly, AI is able to take into account context, not just the shape of the symbol, which helps reduce recognition errors.
One significant step in the development of OCR is the use of Natural Language Processing (NLP) technologies to improve text understanding. After the AI recognizes characters, NLP analyzes the grammar, syntax, and semantic relationships between words. This helps correct possible recognition errors, especially in the case of complex or fuzzy fonts. For example, if OCR has recognized a word with an error, the AI can use the context of the document to suggest the most likely correction.
In addition, AI can recognize not only text but also tables, graphs, or even captions, making OCR a versatile tool for different types of documents. Modern OCR systems can handle different languages and can also learn new fonts and writing styles, making them flexible and adaptive.
In this way, AI helps turn scans and images into useful information, improving the accessibility and efficiency of data processing in areas ranging from archiving and document scanning to processing historical manuscripts and creating databases.