Optical Character Recognition
Optical Character Recognition (OCR) is the technology that converts images of text into machine-readable text data. Modern OCR uses deep learning to handle diverse fonts, handwriting, and document layouts.
Understanding Optical Character Recognition
Optical Character Recognition (OCR) is a technology that converts images of printed or handwritten text into machine-readable digital text, enabling computers to extract and process information from scanned documents, photographs, and PDFs. Modern OCR systems leverage deep learning techniques, particularly convolutional neural networks and transformer architectures, to achieve high accuracy across diverse fonts, languages, and document layouts. Applications include digitizing historical archives, automating invoice and receipt processing, enabling real-time text translation through smartphone cameras, and powering accessibility tools for visually impaired users. Platforms like Google Cloud Vision and Amazon Textract provide OCR as a service through APIs. OCR often works alongside natural language processing pipelines that further analyze the extracted text for named entity recognition, classification, or information retrieval, making it a crucial bridge between the physical and digital worlds.
Category
Computer Vision
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Bounding Box
A bounding box is a rectangular border drawn around an object in an image to indicate its location and extent. Bounding boxes are the primary output format for object detection models.
Computer Vision
Computer vision is a field of AI that enables machines to interpret and understand visual information from images and videos. Applications include facial recognition, autonomous driving, medical imaging, and augmented reality.
Face Recognition
Face recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features in images or video. It is used in security systems, phone unlocking, and photo organization.
Image Captioning
Image captioning is the AI task of generating natural language descriptions of images. It requires both visual understanding (computer vision) and text generation (NLP) capabilities.
Image Classification
Image classification is the computer vision task of assigning a label to an entire image based on its visual content. Deep learning models like ResNet and Vision Transformers achieve near-human accuracy on this task.
Image Segmentation
Image segmentation is the process of partitioning an image into meaningful regions or classifying each pixel into a category. It is used in medical imaging, autonomous driving, and satellite analysis.
Instance Segmentation
Instance segmentation is a computer vision task that identifies each object in an image and delineates its exact pixel boundary. Unlike semantic segmentation, it distinguishes between individual instances of the same class.
Masked Autoencoder
A masked autoencoder is a self-supervised learning method that masks random patches of an image and trains the model to reconstruct them. It has proven highly effective for pre-training vision models.