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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...
The recognition of handwritten characters, words, and text arouses great interest today. To develop the best working system is subject of many papers published. With this paper, methods to improve the performance of existing word recognition systems are discussed. The availability of a sufficient data sets for training and testing the system assumed, optimization algorithms are presented. The usage...
This paper describes the Online Arabic handwriting recognition competition held at ICDAR 2009. This first competition uses the ADAB-database with Arabic online handwritten words. This year, 3 groups with 7 systems are participating in the competition. The systems were tested on known data (sets 1 to 3) and on one test dataset which is unknown to all participants (set 4). The systems are compared on...
This paper describes the online Arabic handwriting recognition competition held at ICDAR 2009. This first competition uses the ADAB-database with Arabic online handwritten words. This year, 3 groups with 7 systems are participating in the competition. The systems were tested on known data (sets 1 to 3) and on one test dataset which is unknown to all participants (set 4). The systems are compared on...
Arabic character and text recognition methods for printed or handwritten characters are known since many years. We present in the first part of this paper a state of the art of Arabic text classification techniques and existing recognition systems. In the second part we discuss how evaluation methods and competitions help to support the development of text recognition systems and methods. Based on...
In this paper we present some methods to combine the outputs of a set of 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 recognizers to a neural network decision based on normalized confidences...
Preprocessing and feature extraction are very important steps in automatic cursive handwritten word recognition. Based on an offline recognition system for Arabic handwritten words which uses a semi-continuous 1-dimensional Hidden Markov Model recognizer, different preprocessing combined with different feature sets are presented. The dependencies of the feature sets from preprocessing steps are discussed...
Given large number of words to be recognized, lexicon reduction strategy for eliminating unlikely candidates before recognition can be a reasonable and powerful approach for increasing the recognition speed. In this paper, we describe a holistic approach for large Arabic handwritten lexicon reduction which is based on inherent properties of Arabic writing. The principal of this technique involves...
This paper describes the Arabic handwriting recognition competition held at ICDAR 2007. This second competition (the first was at ICDAR 2005) again uses the IFN/ENIT-database with Arabic handwritten Tunisian town names. Today, more than 54 research groups from universities, research centers, and industry are working with this database worldwide. This year, 8 groups with 14 systems are participating...
Databases enclosing a huge amount of images of handwritten words together with detailed ground truth information are the most important precondition for the development of handwritten word recognition systems. The IFN/ENIT-database of handwritten Tunisian town names is used by many research groups working on recognition systems. This paper gives at first a short overview about the most important features...
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