Name
Abstract | ||
|---|---|---|
In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1142/S0219467804001373 | Int. J. Image Graphics |
Keywords | Field | DocType |
automation of banking systems.,reading of unconstrained handwritten material,handwritten checks,neural network based reading,working paper,neural network | Architecture,Normalization (statistics),Cashier's check,Pattern recognition,Neural network classifier,Computer science,Courtesy,Segmentation,Speech recognition,Artificial intelligence,Artificial neural network,Syntax | Journal |
Volume | Issue | Citations |
4 | 2 | 14 |
PageRank | References | Authors |
0.67 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Palacios | 1 | 47 | 4.11 |
| Amar Gupta | 2 | 19 | 1.42 |
| patrick s p wang | 3 | 303 | 47.66 |