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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...
In this paper we present a system of the off-line handwriting recognition. Our recognition system is based on temporal order restoration of the off-line trajectory. For this task we use a genetic algorithm (GA) to optimize the sequences of handwritten strokes. To benefit from dynamic informations we make a sampling operation by the consideration of trajectory curvatures. We proceed to calculate the...
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 presents an off-line Arabic handwriting recognition system based on the selection of different state of the art features and the combination of multiple hidden Markov models classifiers. Beside the classical use of the off-line features, we add the use of on-line features and the combination of the developed systems. The designed recognizer is implemented using the HMM-Toolkit. In a first...
This paper describes the handwriting recognition competition held at ICDAR 2009. This competition is based on the RIMES-database, with French written text documents.These document are classified in three different categories,complete text pages, words, and isolated characters. This year 10 systems were submitted for the handwritten recognition competition on snippets of French words. The systems were...
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...
This paper presents a new enhanced text extraction algorithm from degraded document images on the basis of the probabilistic models. The observed document image is considered as a mixture of Gaussian densities which represents the foreground and background document image components. The EM algorithm is introduced in order to estimate and improve the parameters of the mixtures of densities recursively...
The choice of relevant features is very decisive in handwriting recognition rate. Our aim is to present some useful structural and statistical features and see their degree of variability. In this paper, we start with a description of the variability of the Arabic handwriting and the way how to reduce it. Four kinds of feature sets used by our handwriting systems are then presented evaluated and discussed...
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...
In this paper, a system is proposed for word-based recognition of handwritten Arabic scripts. Techniques are discussed in details in terms of three stages in the system, i.e. preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Then, DCT features are extracted for each word sample. Finally, these features are then utilized...
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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