The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Neural networks for pattern recognition
Neural networks for pattern recognition
Optical character recognition
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
WALRUS: A Similarity Retrieval Algorithm for Image Databases
IEEE Transactions on Knowledge and Data Engineering
Modeling and recognition of cursive words with hidden Markov models
Pattern Recognition
Scaling-invariant boundary image matching using time-series matching techniques
Data & Knowledge Engineering
| Hi-index | 0.00 |
Character recognition has been an active research area in the field of pattern recognition. The existing character recognition algorithms are focused mainly on increasing the recognition rate. However, as in the recent Google Library Project, the requirement for speeding up recognition of enormous amount of documents is growing. Moreover, the existing algorithms do not pay enough attention to Asian characters. In this paper, we propose an algorithm for fast recognition of Asian characters based on the database methodologies. Since the number of Asian characters is very large and their shapes are complicated, Asian characters require much more recognition time than numeric and Roman characters. The proposed algorithm extracts the feature from each of Asian characters through the Discrete Fourier Transform (DFT) and optimizes the recognition speed by storing and retrieving the features using a multidimensional index. We improve the recognition speed of the proposed algorithm using the association rule technique, which is a widely adopted data mining technique. The proposed algorithm has the advantage that it can be applied regardless of the language, size, and font of the characters to be recognized.