A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
An Off-Line Cursive Handwriting Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Grammar-Based Recognition of Handwritten Sentences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles applied to handwriting recognition
International Journal on Document Analysis and Recognition
Trainable fusion rules. I. Large sample size case
Neural Networks
Proceedings of the 6th international conference on Multiple Classifier Systems
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Proceedings of the 6th international conference on Multiple Classifier Systems
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Ensemble methods to improve the performance of an English handwritten text line recognizer
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
| Hi-index | 0.00 |
This paper investigates the use of multiple classifier methods for offline handwritten text line recognition. To obtain ensembles of recognisers we implement a random feature subspace method. The word sequences returned by the individual ensemble members are first aligned. Then the final word sequence is produced. For this purpose we use a voting method and two novel statistical combination methods. The conducted experiments show that the proposed multiple classifier methods have the potential to improve the recognition accuracy of single recognisers.