Innovations in Handwriting Recognition Essay

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Emergence networks mimics biological nervous system unleash generations of inventions and discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and Pitts and called neural networks. Neural network’s function is based on principle of extracting the uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed, they gain their experiences from collected samples for known classes (patterns). Quick development of neural networks promotes concept of the pattern recognition by proposing intelligent systems such as handwriting recognition, speech recognition and face recognition. In particular, Problem of handwriting recognition has been considered significantly during …show more content…

The first study discusses the basic operations of erosion and dilation and present a system to recognize six handwritten digits. For this purpose, a novel method to recognize cursive and degraded text has been found by Badr & Haralick (1994 ) through using technology of OCR. Parts of symbols (primitives) are detected to interpret the symbols with this method (KUMAR et al. 2010). The study involves mathematical morphology operations to perform the recognition process. Cun et al. (1998) challenge this problem by designing a neural based classifier to discriminate handwritten numeral. This study achieved a reliable system with very high accuracy (over 99%) on the MINIST database. Moreover, the gradient and curvature of the grey character image have been taken into consideration by Shi et al. (2002 ) to enhance the accuracy of handwriting digits recognition. Uniquely, Teow & Loe (2002 ) identify new idea to solve this problem based on biological vision model with excellent results and very low error rate (0.59%). The discoveries and development have been continuing through innovating new algorithms and learning rules. Besides efficiency of using these rules individually in the machine learning, some researchers have going further in developing the accuracy and performance of the learning by mixing several rules to support one

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