|Author:||Tsang, Chi-hang Peter|
|Title:||On-line recognition of digits and alphabets|
|Other Title:||Online recognition of digits and alphabets|
|Subject:||Optical pattern recognition|
Writing -- Data processing
Hong Kong Polytechnic University -- Dissertations
Department of Electronic and Information Engineering
|Pages:||vi, 81 leaves : ill. ; 30 cm|
|Abstract:||Handwriting is a natural and fast way to input words to computers. Computer recognition of on-line handwriting offers a new way of improving the human-computer interface. The objective of this project is to study and implement efficient algorithms for recognising handwriting digits and alphabets. The system includes handwriting character capturing, feature extraction, off-line/on-line neural network training, and recognition of handwriting characters. Features used for recognition can be categorised into two groups: off-line and on-line features. On-line recognition makes use of pen trajectory data. Also spatially overlapping characters do not pose a serious segmentation problem as they are still separable by using the on-line information. On the other hand, on-line recognition should have a reliable and robust recognition unit. This can be achieved by using both on-line and off-line features, which are discussed in this dissertation. A multi-layer feedforward neural network is trained to recognise 62 handwriting characters (10 digits, 26 lowercase alphabets, and 26 uppercase alphabets) using the error back-propagation algorithm with a training data set of five samples for each character. Different weight vectors are trained for different writers. Experimental results show that the system can achieve a correct rate of about 89% for user-dependent recognition.|
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