000			01940nam a22002297a 4500
999			_c75378 _d75378
003			CITU
005			20211123164453.0
008			210913b ||||| |||| 00| 0 eng d
082			_a621.39/9
100	1		_aSuson, Jennelyn C. _eauthor
245			_aNon-cursive handwritten word recognizer using neural network / _cJennelyn C. Suson.
300			_aviii, 135 leaves : _c28 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
500			_aComputer print-out
502			_aThesis (Master in Computer Science) -- Cebu Institute of Technology - University, March 2011.
520			_aHandwriting recognition is a challenging task for many real-world applications such as document authentication, form processing, postal address recognition, bank check recognition, and interpretation of historical documents. With this, researchers have put an enormous effort into developing various techniques for handwriting recognition. This study presents a non-cursive handwritten word recognition system using neural network. This system has the ability to receive handwritten word from an input device. This handwritten word is preprocessed, cropped and segmented, and then feed into the neural network of training, classification and recognition. Handwritten word capturing of the system is done using a mouse-an input device, and is sensed online, that is, automatic conversion of word as it is written. Kohonen Self-Organizing Map (Kohonen SOM) neural network and image processing are the two main techniques applied. This study ensures that the processed word image data is read exactly the same with the original non-cursive handwritten word. The results show a practical application of the Kohonen neural network and are to what uses this technology might have.
650		0	_aOptical character recognition devices.
650		0	_aWriting _xData processing.
942			_2ddc _cT&D