What in computer science is OCR?

OCR (Optical Character Recognition) is a technology that enables computers to extract and interpret text from images, scanned documents, or other visual sources. It converts unstructured text in formats like JPEGs, PDFs, or handwritten notes into machine-readable and editable data. At its core, OCR identifies patterns in pixel data to recognize characters, words, and sentences, bridging the gap between physical or visual text and digital systems. This process is fundamental for automating tasks that involve processing printed or written information.

A typical OCR system involves multiple steps. First, preprocessing cleans the input image by adjusting contrast, removing noise, or deskewing tilted text. Next, text detection locates regions of interest, separating text from backgrounds or graphics. Modern OCR tools like Tesseract or cloud-based services (e.g., Google Vision API) then use machine learning models, such as convolutional neural networks (CNNs), to classify individual characters or entire words. For example, a developer might use Python’s pytesseract library to extract text from a scanned invoice image, transforming it into a string that can be stored in a database or analyzed programmatically. Handwriting recognition adds complexity, often requiring trained models tailored to specific styles or languages.

OCR has widespread applications across industries. Banking apps use it to scan checks and extract account numbers, while logistics companies automate package tracking by reading barcodes or shipping labels. Developers might integrate OCR into mobile apps for real-time translation—like capturing street signs with a phone camera and converting the text to another language. Challenges include handling low-resolution images, unusual fonts, or overlapping text. To improve accuracy, developers often combine OCR with post-processing rules (e.g., regex for dates) or contextual NLP models. While tools like AWS Textract or Azure Cognitive Services simplify implementation, understanding limitations—such as reliance on image quality—is critical for building robust systems.