Name
Abstract | ||
|---|---|---|
This paper presents a system for the recognition of the handwritten Indian numerals one to nine (1–9) using a probabilistic neural network (PNN) approach. The process involved extracting a feature vector to represent the handwritten digit based on the center of gravity and a set of vectors to the boundary points of the digit object. The feature vector is scale-, translation-, and rotation-invariant. The extracted feature vector is fed to a PNN, which in turn classifies it as one of the nine digits. A set of experiments were conducted to test the performance of the system under different angles between the vectors from the centroid to the boundary of the digit object. A 30° angle results in a 99.72% recognition rate with a short feature vector of 12 entries. This study is meant to be a seed toward building a recognition system for Arabic language characters. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.aei.2004.02.001 | Advanced Engineering Informatics |
Keywords | Field | DocType |
Character recognition,Handwritten digit recognition,Pattern recognition,Probabilistic neural networks,Image segmentation,Artificial intelligence | Neocognitron,Indian numerals,Feature vector,Pattern recognition,Computer science,Speech recognition,Probabilistic neural network,Feature (machine learning),Artificial intelligence,Artificial neural network,Centroid,Neural gas | Journal |
Volume | Issue | ISSN |
18 | 1 | 1474-0346 |
Citations | PageRank | References |
25 | 1.22 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faruq A. Al-Omari | 1 | 42 | 5.07 |
| Omar M. Al-jarrah | 2 | 204 | 29.55 |