What is optical character recognition (OCR) in computer vision?

Optical Character Recognition (OCR) is a technology used in computer vision to detect and extract text from images, scanned documents, or other visual sources, converting it into machine-readable and editable text. At its core, OCR enables computers to interpret characters (letters, numbers, symbols) in pixel-based data, transforming unstructured image data into structured text. This process is essential for automating tasks that involve processing printed or handwritten text, such as digitizing paper records, extracting information from invoices, or enabling text search in scanned PDFs. OCR systems typically work by analyzing the shapes and patterns in an image, identifying regions containing text, and translating those visual representations into encoded characters.

The technical workflow of OCR involves multiple steps. First, the input image is preprocessed to improve text detection—for example, by converting it to grayscale, enhancing contrast, or removing noise. Next, text detection algorithms locate regions of interest (like paragraphs, lines, or individual characters) using techniques such as contour detection or deep learning-based object detection models. Once text regions are identified, recognition algorithms classify each character. Traditional OCR systems rely on feature extraction and pattern matching, while modern approaches use convolutional neural networks (CNNs) or transformer-based models trained on large datasets of labeled text. For example, Tesseract, a widely used open-source OCR engine, combines segmentation and language modeling to improve accuracy. Challenges include handling variations in fonts, text orientation, low-resolution images, or overlapping backgrounds, which require robust preprocessing and model tuning.

OCR has practical applications across industries. Developers might integrate it into mobile apps for scanning business cards, into document management systems to automate data entry from forms, or into accessibility tools to read text aloud for visually impaired users. Libraries like Google’s Vision AI, AWS Textract, or PyTesseract provide APIs and SDKs to simplify implementation. However, accuracy depends on factors like image quality, language support, and text complexity. For instance, processing a blurry restaurant menu with decorative fonts may require additional steps like image sharpening or custom model training. Developers often combine OCR with post-processing techniques—such as spell-checking or regular expressions—to refine results. While OCR is a mature technology, ongoing advancements in deep learning continue to address limitations, such as recognizing handwritten text or multilingual documents, making it a versatile tool in modern software systems.