Optical character recognition (OCR) is a technology that is used to convert printed or handwritten text into machine-readable text. OCR software and systems are designed to recognize and extract characters, words, and text from images, scanned documents, or even live video streams. This technology is particularly useful for digitizing printed documents and making them searchable and editable.
Here are some key aspects of OCR:
Input sources: OCR can process text from various sources, including scanned documents, images, photos, PDFs, or even text in real-time from a camera or video feed.
Text extraction: OCR software analyzes the input and identifies individual characters, words, and text blocks. It then converts them into machine-readable text.
Language support: OCR systems can recognize text in multiple languages and character sets, making it a versatile tool for handling documents in various languages.
Accuracy: The accuracy of OCR can vary depending on the quality of the source material, the clarity of the text, and the quality of the OCR software or service. Modern OCR systems can achieve high accuracy rates, but they may still require human proofreading for critical applications.
Applications: OCR has a wide range of applications, including document digitization, data entry, text translation, and making printed content accessible to individuals with visual impairments. It is also commonly used in document management systems, archiving, and information retrieval.
Handwriting recognition: Some OCR systems are capable of recognizing handwritten text, although the accuracy for handwritten text can be lower compared to printed text.
Preprocessing: OCR often involves preprocessing steps such as image enhancement, noise reduction, and layout analysis to improve accuracy.
OCR engines: Several OCR engines and libraries are available, both as standalone software and as part of larger software suites. Tesseract, ABBYY FineReader, and Adobe Acrobat are examples of popular OCR software tools.
Machine learning: Many modern OCR systems use machine learning techniques, such as deep learning and neural networks, to improve accuracy and handle complex fonts and layouts.
OCR in mobile apps: OCR technology is commonly integrated into mobile apps for tasks like scanning documents, recognizing text on images, and translating text.