Name
Abstract | ||
|---|---|---|
This paper explores the use of ANN (artificial neural networks) in handwriting recognition. The approach has been found to be very suitable for handwritten character recognition as it provides fast feature extraction and classification. Using the EBP (error backpropagation) algorithm, networks of relatively small sizes (ones requiring modest memory requirements) which can be trained in a reasonably short time were used. The recognition accuracy of the system has been found to be more than 97% with a response speed of about 1 character per second |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/KES.1997.616872 | Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference |
Keywords | Field | DocType |
backpropagation,feature extraction,handwriting recognition,image classification,neural nets,optical character recognition,performance evaluation,artificial neural network,classification,error backpropagation,feature extraction,handwriting recognition,handwritten character recognition,memory requirements,recognition accuracy,response speed,time | Neocognitron,Pattern recognition,Intelligent character recognition,Computer science,Optical character recognition,Handwriting recognition,Speech recognition,Feature extraction,Time delay neural network,Artificial intelligence,Artificial neural network,Backpropagation | Conference |
Volume | ISBN | Citations |
1 | 0-7803-3755-7 | 2 |
PageRank | References | Authors |
0.37 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Goh, W.L. | 1 | 2 | 0.37 |
| Dinesh P. Mital | 2 | 40 | 4.71 |
| Babri, H.A. | 3 | 6 | 1.48 |