Ai in Optical Character Recognition

Topics: Artificial intelligence, Optical character recognition, Intelligent character recognition Pages: 2 (451 words) Published: February 17, 2011
AI in optical character recognition
Artificial intelligence (AI) is the intelligence of machines. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.

There are different fields under optical character recognition. Intelligent character recognition
Handwriting recognition
Automatic number plate recognition

In computer science, intelligent character recognition (ICR) is an advanced optical character recognition (OCR) or — rather more specific — handwriting recognition system that allows fonts and different styles of handwriting to be learned by a computer during processing to improve accuracy and recognition levels. Most ICR software has a self-learning system referred to as a neural network, which automatically updates the recognition database for new handwriting patterns. Because this process is involved in recognizing hand writing, accuracy levels may, in some circumstances, not be very good but can achieve 97%+ accuracy rates in reading handwriting in structured forms. Often to achieve these high recognition rates several read engines are used within the software and each is given elective voting rights to determine the true reading of characters.

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems

Neural network recognizers learn from an initial image training set. The trained network then makes the character identifications. Each neural network uniquely learns the properties that differentiate training images. It then looks for similar properties in the...
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