Artificial neural networks are biologically motivated. They are reserve functions of the human brain. Neural networks are parallel computing devices, which primarily seek to build a computer model of the brain. Parallel processing is the brain's ability to do multiple things at the same time. For example, when a human being sees an object he does not observe just one thing, but many different aspects which together help the person to recognize the object in its entirety. The main purpose is to extend a system to perform various computational tasks faster than traditional systems. These tasks consist of pattern recognition and classification, data approximation, optimization, and clustering. Neural networks are successfully applied to a large scale of data-intensive applications. There are several categories: financial, energy, industrial, science, data mining, sales and marketing, operational analytics, human resource management and medical. Pattern Recognition falls into the Science category. Character recognition is a fascinating dilemma that falls into the common area of ​​Pattern Recognition. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Visual character recognition is known to be one of the most primitive applications of artificial neural network, which somewhat imitates human thinking in the field of Artificial Intelligence. Many neural networks are developed for the automatic detection of hand-typed characters, be they letters or numbers. Multilayer neural networks like backpropagation neural networks, Neocognitron are different ANNs used for character recognition. The neocognitron is a multilayer hierarchical artificial neural network proposed by Kunihhiko Fukushima in 1980. Backpropagation is an efficient method used in artificial neural networks to estimate the fault contribution of each neuron after processing a set of data. It is used for training multilayer artificial neural networks. Rumelhart, Hinton, and Williams (1986) presented a neat and clear explanation of the backpropagation algorithm. Although backpropagation neural networks have numerous hidden layers, the connection pattern from one layer to the next is localized. Similarly, neocognitron also has several hidden layers and its training is complete level by level for this type of applications. OCR (optical character recognition or optical character reader) is usually an "offline" process, which analyzes a static act. Motion analysis of handwriting could be used as input for handwriting recognition. Instead of simply using the shapes of glyphs and words, this method can capture gestures, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it up. This additional information can make the end-to-end process more perfect. This technology is also known as “inline character recognition”, “dynamic character recognition”, “real-time character recognition” and “intelligent character recognition”.”.”.