Optical Character Recognition (OCR) in computer vision is a technology used to convert different types of documents—such as scanned paper documents, PDFs, or images of typed or handwritten text—into editable and searchable data. OCR works by analyzing the structure of the text in the image, segmenting it into individual characters or words, and then using machine learning algorithms to match these segments with the corresponding characters in a predefined character set. OCR is commonly used in document digitization, invoice processing, and automated data entry. Advanced OCR systems, such as Tesseract and Adobe Acrobat, utilize techniques like deep learning to improve the accuracy of text recognition, even in complex or noisy images. OCR is also capable of recognizing different fonts, handwriting, and languages, making it a powerful tool for extracting information from a wide range of textual sources. The integration of OCR with other computer vision tasks, such as object detection or scene analysis, can further enhance its capabilities in real-world applications.
What is optical character recognition (OCR) in computer vision?
Keep Reading
How does full-text search support filtering?
Full-text search supports filtering by allowing users to refine their search results based on specific criteria or attri
Data Lake vs Data Warehouse vs Data Lakehouse: How They Differ and When to Use Each
# Data Lake vs Data Warehouse vs Data Lakehouse: How They Differ and When to Use Each
**Last updated: 2026-06-09** · By
How does query expansion handle ambiguity?
Query expansion is a technique used to improve the clarity of search queries by adding additional terms that express the