OCR, abbreviated as Optical Character Recognition, is a technology that converts text images into machine-readable text formats. Primarily, it detects printed or handwritten text in digital images. OCR involves using a scanner to capture text from images, and AI-powered software identifies the languages and writing styles.

Python is commonly used for OCR development as it is well-suited for computer vision tasks. Python OCR technology, often using the open-source OCR engine Tesseract, provides extensive support for deep learning and computer vision applications.

Semantic segmentation is the process of categorizing every pixel in an image into predefined classes, treating multiple objects within a class as a single entity. It effectively labels pixels to identify objects like cars, people, etc., based on pixel boundaries.

Semantic segmentation groups objects into classes or categories, while instance segmentation distinguishes individual instances within a class. Semantic segmentation identifies object classes, whereas instance segmentation pinpoints specific objects within those classes.

Pattern detection classifies data by analyzing extracted information and identifying patterns with AI and ML tools. It’s widely used for predictions and decision-making improvements.

AI-powered image generators use Machine Learning (ML) algorithms like Artificial Neural Networks (ANN) to create realistic images. They interpret text prompts, learning from input data, and generate images by mimicking styles and structures.

Generative AI models with neural networks identify patterns and structures from large datasets, combining Machine Learning algorithms to generate images according to input parameters.

Yes! Many AI image generators allow free image creation for websites, producing unique and realistic images that aren’t available in libraries. These tools enable creation using just text and imagination for icons, 3D objects, or any visual assets.