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Pattern recognition is the process of identifying recurring structures or regularities within data. It is a fundamental aspect of human cognition, enabling us to make sense of the world around us. In computational terms, pattern recognition involves algorithms and techniques designed to detect and classify these patterns. This field draws heavily from statistics, machine learning, and artificial intelligence.

The goal of pattern recognition is to classify input data into predefined categories based on extracted features. These features are measurable characteristics that help distinguish one pattern from another. For example, in image recognition, features might include edges, textures, or colors. Once features are extracted, various algorithms, such as neural networks or support vector machines, are employed to learn from labeled data and make predictions on new, unseen data.

Applications of pattern recognition are widespread. It is crucial in areas like speech recognition, where spoken words are converted into text; optical character recognition (OCR), which converts scanned documents into editable text; medical diagnosis, by identifying anomalies in medical images; and fraud detection, by spotting unusual transaction patterns. The effectiveness of a pattern recognition system depends on the quality of the data, the choice of features, and the suitability of the classification algorithm for the specific problem.