A Framework for the Combination of Different Arabic Handwritten Word Recognition Systems

In this paper we present A Framework for the Combination of Different Arabic Handwritten Word Recognition Systems to achieve a decision with a higher performance. This performance can be expressed by lower rejection rates and higher recognition rates. The used methods range from voting schemes based on results of different recognizer to a neural network decision based on normalized confidences. This work presents an extension of the well known combination methods for a large lexicon, an extension from maximum 30 classes (e.g., 10 classes for digits classification) to 937 classes for the IfN/ENIT-database. In addition, different reject rules based on the evaluation and analysis of individual and combined systems output are discussed. Different threshold function for reject levels are tested and evaluated. Tests with a set of recognizer, which participated in the ICDAR 2007 competition and based on set coming from the IfN/ENIT-database show that a word error rate (WER) of 5.29% without reject and with a reject rate less than 25% even a word error rate of less than 1%.