Nous proposons dans cet article une méthode de fusion des images multispectrales et radar par l&#... more Nous proposons dans cet article une méthode de fusion des images multispectrales et radar par l'utilisation conjointe de l'espace des couleurs et de la transformation en ondelettes. La transformation de l'espace des couleurs permet une séparation des informations spatiale (composante lumineuse) et spectrale (composantes teinte et saturation). La substitution de la composante luminance par une combinaison luminance-radar permet de produire des images fusionnées multispectrale-radar. Celle-ci est produite au moyen d'un processus de fusion appliqué dans le plan des ondelettes pour exploiter les informations contenues dans les images luminance et radar. Pour cela, nous développons un modèle de fusion dépendant des coefficients d'ondelettes et d'un paramètre de fusion qui permet de régler le poids relatif des informations luminance et radar dans le processus de fusion. Un tel paramètre dépend de l'énergie des coefficients qui constitue un bon indicateur de l&#...
2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS), 2018
The character recognition from natural scene image is used in many applications as for instance i... more The character recognition from natural scene image is used in many applications as for instance in intelligent transportation systems. Indeed, many recent methods have been focused to design a better feature representation of the character. However, the performances of such system is still far from perfect since it depends also of the appropriate choice of the classifier. Hence, the present paper addresses the problem of natural character recognition and tries to investigate the performance of the One Class-Principal Component Analysis Classifier (OC-PCA). The OC-PCA classifier has the main advantage to absorb the high dimension of the feature vector with few samples. For evaluating the performance of the proposed classifier, experimental results are conducted on challenging Char74k dataset highlighting its robustness against the state-of-art.
It gives us immense pleasure to introduce the proceedings of the first edition of the Mediterrane... more It gives us immense pleasure to introduce the proceedings of the first edition of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI-2016) held on November 22-23, 2016, in Tebessa, Algeria. The event was aimed at providing an interdisciplinary forum of discussion to share the recent advancements in different areas of pattern recognition and artificial intelligence and was endorsed by the International Association of Pattern Recognition (IAPR). This volume of proceedings contains the papers presented at the conference.
2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2017
In this work, we propose a new descriptor that is called Gradient Local Binary Patterns (GLBP) fo... more In this work, we propose a new descriptor that is called Gradient Local Binary Patterns (GLBP) for automatic keyword spotting in handwritten documents. GLBP is a gradient feature that improves the Histogram of Oriented Gradients (HOG) by calculating the gradient information at transitions of the Local Binary Pattern code. For the matching step, we use the Euclidian Distance and the Cosine Similarity. To show GLBP's performance, we used a Benchmark dataset which contains 100 documents written if 4 languages, from those documents 300 query were extracted to be spotted. The results obtained highlight the effectiveness of the proposed descriptor.
2018 Eighth International Conference on Information Science and Technology (ICIST), 2018
In order to take advantage from collections of digitized handwritten documents, effective indexin... more In order to take advantage from collections of digitized handwritten documents, effective indexing and retrieval techniques are required. This work focuses on automatic writer retrieval, which is the task of finding in a dataset, all documents written by the same person. Contrary to conventional writer retrieval techniques that are based on dissimilarity measures, we propose to use the SVM classifier to perform the retrieval task. First, local gradient features are used to generate handwritten features. Then, dissimilarities calculated between intra-writer and inter-writer documents are used to train a SVM to allow an automatic retrieval of all the writers documents. Experiments are conducted on CVL and ICDAR 2011 datasets. The performance evaluation of the proposed system is carried out comparatively to the cosine similarity. Results obtained evince a significant improvement offered by SVM, which gives comparable and sometimes better scores than the state of the art.
In this work, a system for solving handwritten Arabic word recognition is proposed. The aim is fo... more In this work, a system for solving handwritten Arabic word recognition is proposed. The aim is focused on holistic word recognition, which is devoted to recognize averaged size lexicons by using a single classifier. Presently, we investigate the applicability of the Artificial Immune Recognition System (AIRS) to achieve the recognition task. For the feature generation step, ridgelet transform and pixel density features are combined to highlight both linear singularities and topological traits of Arabic words. Experiments are conducted on a vocabulary of twenty-four words extracted from the IFN/ENIT dataset. The results show that feature combination improves the recognition accuracy with more than 1%. The comparison with Support Vector Machine (SVM) classifier highlights the effectiveness of AIRS. This latter achieves comparable and sometimes better performance than SVM and can be extended to recognize any number of classes.
One-class classifier (OCC) is involved for solving different kinds of problems due to its ability... more One-class classifier (OCC) is involved for solving different kinds of problems due to its ability to represent a class distribution regardless the remaining classes. Its main advantage for multi-class classification is offering an open system and therefore allows easily extending new classes without retraining OCCs. So far, hidden Markov models, support vector machines and neural networks are the most used classifiers for Arabic word recognition, which provides a system with closed lexicon. In this paper, the OCCs are explored in order to perform an Arabic word recognition system with an open lexicon. Generally, pattern recognition systems designed by a single system suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining multiple systems becomes an attractive research topic for performance and robustness enhancement. Fixed rules are commonly used us combiners for the hybrid OCC ensembles. The present paper aims to propose a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Furthermore, an alternative framework is proposed to design a parameter-independent and open-lexicon handwritten Arabic word recognition system as well as a new density measure function. Experimental results conducted on Arabic handwritten dataset using different types of OCCs with large number of classes highlight the superiority of FI for hybrid OCC ensembles.
This work proposes a novel system for off-line handwritten signature verification. A new descript... more This work proposes a novel system for off-line handwritten signature verification. A new descriptor founded on a quad-tree structure of the Histogram Of Templates (HOT) is introduced. For the verification step, we propose a robust implementation of the Artificial Immune Recognition System (AIRS). This classifier is inspired from the natural immune system, which generates antibodies to protect the human body against antigens. The AIRS training develops new memory cells that are subsequently used to recognize data through a k Nearest Neighbor (kNN) classification. Presently, to get a robust verification, the kNN classification is substituted by a Support Vector (SV) decision, yielding the AIRSV classifier. Experiments are performed on three datasets, namely, MCYT-75, GPDS-300 and GPDS-4000. AIRSV performance is assessed comparatively to both conventional AIRS as well as SVM. Obtained results demonstrated that AIRSV is more effective than classical AIRS. Moreover, the proposed signature verification system gives similar and sometimes better performance than SVM as well as the state-of-the-art methods.
The segmentation of handwritten digit strings into isolated digits remains a challenging task. Th... more The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-length handwritten digit strings by combining several explicit segmentation methods depending on the configuration link between digits. Three segmentation methods are combined based on histogram of the vertical projection, the contour analysis and the sliding window Radon transform. A recognition and verification module based on support vector machine classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Moreover, various submodules are included leading to enhance the robustness of the proposed system. Experimental results conducted on the benchmark dataset show that the proposed system is effective for segmenting handwritten digit strings without prior knowledge of their length comparatively to the state of the art.
2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015
Most of the classical threshold-based methods for document image binarization use simple features... more Most of the classical threshold-based methods for document image binarization use simple features carried out from the spatial pixels values of the document images. In this paper, we present a new binarization method for degraded documents, based on Local Binary Pattern (LBP) as a texture measure. The mean and variance of pixels are computed respectively from both the original document image and the LBP image. Then, these features are used within a threshold-based method. Another variant is computed by combining a contrast information with the LBP operator to overcome the drawback caused by the poor contrasted document images. Experimental results conducted on DIBCO datasets and compared against some state-of-the-art methods, prove the effective use of the LBP for binarizing historical documents.
We propose in this work a signature verification system based on decision combination of off-line... more We propose in this work a signature verification system based on decision combination of off-line signatures for<br> managing conflict provided by the SVM classifiers. The system is basically divided into three modules: i) Radon Transform-SVM, ii) Ridgelet Transform-SVM and iii) PCR5 combination rule based on the generalized belief functions of Dezert-Smarandache theory.
Proceedings of the International Conference on Computing for Engineering and Sciences, 2017
Several approaches for handwritten digits recognition are proposed an appearance feature-based ap... more Several approaches for handwritten digits recognition are proposed an appearance feature-based approach. In this paper we process handwritten digit image without deskewing using oriented Basic Image Features (oBIF) Column scheme extracted from the complete image as well as from different regions of the image by applying a uniform grid sampling to the image. oBIF Column scheme is a very efficient feature descriptor for handwritten digits which is arise from variations in size, shape and slant. Moreover, 4th Nearest Neighbor (4-NN) has been employed as classifier which has better responses. The experimental study is conducted on MNIST dataset and 98.32% recognition rate has been achieved which is comparable with the state of the art.
2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020
The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen t... more The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen the writer retrieval in historical document databases. Various CNN models that are LeNet, ResNet, and VGG are used to get writer-independent features of handwritten documents. These features are associated with dissimilarity measures to achieve the retrieval task. Then, developed writer retrieval systems are combined through SVM classifier. Experiments are conducted on ICDAR-2017 dataset which contains historical handwritten documents. The results obtained highlight the robustness of CNN features and the combination stage as well.
2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020
Developing robust signature verification systems is one of the most attracting topics in the hand... more Developing robust signature verification systems is one of the most attracting topics in the handwriting recognition field. In fact, dealing with signature spoofing requires the use of robust features that help to distinguish authentic signatures from the forged ones. Presently, we propose a multiscale fusion of two histogram-based features to perform signatures description. Precisely, we introduce the Local Difference Feature as new descriptor that is fused with the Histogram Of Templates. These features are calculated on a multiscale neighborhood to highlight pixels distribution within the signature shape. The verification stage is achieved by using SVM classifier. Performance assessment is carried out on GPDS-300 and MCYT-75 datasets. Results in terms of average error rates evince the robustness of the proposed features, which outperform various state of the art methods.
Computational Intelligence and Its Applications, 2018
Verifying the authenticity of handwritten signatures is required in various current life domains,... more Verifying the authenticity of handwritten signatures is required in various current life domains, notably with official contracts, banking or financial transactions. Therefore, in this paper a novel histogrambased descriptor and an improved classification of the bio-inspired Artificial Immune Recognition System (AIRS) are proposed for handwritten signature verification. Precisely, the Histogram Of Templates (HOT) is introduced to characterize the most widespread orientations of local strokes in handwritten signatures, while the combination of AIRS and SVM is proposed to achieve the verification task. Usually, using the k Nearest Neighbor rule, a questioned signature is classified by computing dissimilarities with respect to all AIRS outputs. In this work, using these dissimilarities, a second round of training is achieved by the SVM classifier to further improve the discrimination power. In comparison with existing methods, the experiments on two widely-used datasets show the potential and the effectiveness of the proposed system.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights An open handwritten identification system is proposed based on one-class classifier. Two methods for feature generation are proposed based on the Curvelet transform. A scheme using Fuzzy Integral (FI) is proposed for combining individual systems. A new density estimation is proposed to better adapt FI for one-class ensemble. A design framework is proposed for parameter-independent open identification system.
Nous proposons dans cet article une méthode de fusion des images multispectrales et radar par l&#... more Nous proposons dans cet article une méthode de fusion des images multispectrales et radar par l'utilisation conjointe de l'espace des couleurs et de la transformation en ondelettes. La transformation de l'espace des couleurs permet une séparation des informations spatiale (composante lumineuse) et spectrale (composantes teinte et saturation). La substitution de la composante luminance par une combinaison luminance-radar permet de produire des images fusionnées multispectrale-radar. Celle-ci est produite au moyen d'un processus de fusion appliqué dans le plan des ondelettes pour exploiter les informations contenues dans les images luminance et radar. Pour cela, nous développons un modèle de fusion dépendant des coefficients d'ondelettes et d'un paramètre de fusion qui permet de régler le poids relatif des informations luminance et radar dans le processus de fusion. Un tel paramètre dépend de l'énergie des coefficients qui constitue un bon indicateur de l&#...
2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS), 2018
The character recognition from natural scene image is used in many applications as for instance i... more The character recognition from natural scene image is used in many applications as for instance in intelligent transportation systems. Indeed, many recent methods have been focused to design a better feature representation of the character. However, the performances of such system is still far from perfect since it depends also of the appropriate choice of the classifier. Hence, the present paper addresses the problem of natural character recognition and tries to investigate the performance of the One Class-Principal Component Analysis Classifier (OC-PCA). The OC-PCA classifier has the main advantage to absorb the high dimension of the feature vector with few samples. For evaluating the performance of the proposed classifier, experimental results are conducted on challenging Char74k dataset highlighting its robustness against the state-of-art.
It gives us immense pleasure to introduce the proceedings of the first edition of the Mediterrane... more It gives us immense pleasure to introduce the proceedings of the first edition of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI-2016) held on November 22-23, 2016, in Tebessa, Algeria. The event was aimed at providing an interdisciplinary forum of discussion to share the recent advancements in different areas of pattern recognition and artificial intelligence and was endorsed by the International Association of Pattern Recognition (IAPR). This volume of proceedings contains the papers presented at the conference.
2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2017
In this work, we propose a new descriptor that is called Gradient Local Binary Patterns (GLBP) fo... more In this work, we propose a new descriptor that is called Gradient Local Binary Patterns (GLBP) for automatic keyword spotting in handwritten documents. GLBP is a gradient feature that improves the Histogram of Oriented Gradients (HOG) by calculating the gradient information at transitions of the Local Binary Pattern code. For the matching step, we use the Euclidian Distance and the Cosine Similarity. To show GLBP's performance, we used a Benchmark dataset which contains 100 documents written if 4 languages, from those documents 300 query were extracted to be spotted. The results obtained highlight the effectiveness of the proposed descriptor.
2018 Eighth International Conference on Information Science and Technology (ICIST), 2018
In order to take advantage from collections of digitized handwritten documents, effective indexin... more In order to take advantage from collections of digitized handwritten documents, effective indexing and retrieval techniques are required. This work focuses on automatic writer retrieval, which is the task of finding in a dataset, all documents written by the same person. Contrary to conventional writer retrieval techniques that are based on dissimilarity measures, we propose to use the SVM classifier to perform the retrieval task. First, local gradient features are used to generate handwritten features. Then, dissimilarities calculated between intra-writer and inter-writer documents are used to train a SVM to allow an automatic retrieval of all the writers documents. Experiments are conducted on CVL and ICDAR 2011 datasets. The performance evaluation of the proposed system is carried out comparatively to the cosine similarity. Results obtained evince a significant improvement offered by SVM, which gives comparable and sometimes better scores than the state of the art.
In this work, a system for solving handwritten Arabic word recognition is proposed. The aim is fo... more In this work, a system for solving handwritten Arabic word recognition is proposed. The aim is focused on holistic word recognition, which is devoted to recognize averaged size lexicons by using a single classifier. Presently, we investigate the applicability of the Artificial Immune Recognition System (AIRS) to achieve the recognition task. For the feature generation step, ridgelet transform and pixel density features are combined to highlight both linear singularities and topological traits of Arabic words. Experiments are conducted on a vocabulary of twenty-four words extracted from the IFN/ENIT dataset. The results show that feature combination improves the recognition accuracy with more than 1%. The comparison with Support Vector Machine (SVM) classifier highlights the effectiveness of AIRS. This latter achieves comparable and sometimes better performance than SVM and can be extended to recognize any number of classes.
One-class classifier (OCC) is involved for solving different kinds of problems due to its ability... more One-class classifier (OCC) is involved for solving different kinds of problems due to its ability to represent a class distribution regardless the remaining classes. Its main advantage for multi-class classification is offering an open system and therefore allows easily extending new classes without retraining OCCs. So far, hidden Markov models, support vector machines and neural networks are the most used classifiers for Arabic word recognition, which provides a system with closed lexicon. In this paper, the OCCs are explored in order to perform an Arabic word recognition system with an open lexicon. Generally, pattern recognition systems designed by a single system suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining multiple systems becomes an attractive research topic for performance and robustness enhancement. Fixed rules are commonly used us combiners for the hybrid OCC ensembles. The present paper aims to propose a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Furthermore, an alternative framework is proposed to design a parameter-independent and open-lexicon handwritten Arabic word recognition system as well as a new density measure function. Experimental results conducted on Arabic handwritten dataset using different types of OCCs with large number of classes highlight the superiority of FI for hybrid OCC ensembles.
This work proposes a novel system for off-line handwritten signature verification. A new descript... more This work proposes a novel system for off-line handwritten signature verification. A new descriptor founded on a quad-tree structure of the Histogram Of Templates (HOT) is introduced. For the verification step, we propose a robust implementation of the Artificial Immune Recognition System (AIRS). This classifier is inspired from the natural immune system, which generates antibodies to protect the human body against antigens. The AIRS training develops new memory cells that are subsequently used to recognize data through a k Nearest Neighbor (kNN) classification. Presently, to get a robust verification, the kNN classification is substituted by a Support Vector (SV) decision, yielding the AIRSV classifier. Experiments are performed on three datasets, namely, MCYT-75, GPDS-300 and GPDS-4000. AIRSV performance is assessed comparatively to both conventional AIRS as well as SVM. Obtained results demonstrated that AIRSV is more effective than classical AIRS. Moreover, the proposed signature verification system gives similar and sometimes better performance than SVM as well as the state-of-the-art methods.
The segmentation of handwritten digit strings into isolated digits remains a challenging task. Th... more The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-length handwritten digit strings by combining several explicit segmentation methods depending on the configuration link between digits. Three segmentation methods are combined based on histogram of the vertical projection, the contour analysis and the sliding window Radon transform. A recognition and verification module based on support vector machine classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Moreover, various submodules are included leading to enhance the robustness of the proposed system. Experimental results conducted on the benchmark dataset show that the proposed system is effective for segmenting handwritten digit strings without prior knowledge of their length comparatively to the state of the art.
2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015
Most of the classical threshold-based methods for document image binarization use simple features... more Most of the classical threshold-based methods for document image binarization use simple features carried out from the spatial pixels values of the document images. In this paper, we present a new binarization method for degraded documents, based on Local Binary Pattern (LBP) as a texture measure. The mean and variance of pixels are computed respectively from both the original document image and the LBP image. Then, these features are used within a threshold-based method. Another variant is computed by combining a contrast information with the LBP operator to overcome the drawback caused by the poor contrasted document images. Experimental results conducted on DIBCO datasets and compared against some state-of-the-art methods, prove the effective use of the LBP for binarizing historical documents.
We propose in this work a signature verification system based on decision combination of off-line... more We propose in this work a signature verification system based on decision combination of off-line signatures for<br> managing conflict provided by the SVM classifiers. The system is basically divided into three modules: i) Radon Transform-SVM, ii) Ridgelet Transform-SVM and iii) PCR5 combination rule based on the generalized belief functions of Dezert-Smarandache theory.
Proceedings of the International Conference on Computing for Engineering and Sciences, 2017
Several approaches for handwritten digits recognition are proposed an appearance feature-based ap... more Several approaches for handwritten digits recognition are proposed an appearance feature-based approach. In this paper we process handwritten digit image without deskewing using oriented Basic Image Features (oBIF) Column scheme extracted from the complete image as well as from different regions of the image by applying a uniform grid sampling to the image. oBIF Column scheme is a very efficient feature descriptor for handwritten digits which is arise from variations in size, shape and slant. Moreover, 4th Nearest Neighbor (4-NN) has been employed as classifier which has better responses. The experimental study is conducted on MNIST dataset and 98.32% recognition rate has been achieved which is comparable with the state of the art.
2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020
The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen t... more The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen the writer retrieval in historical document databases. Various CNN models that are LeNet, ResNet, and VGG are used to get writer-independent features of handwritten documents. These features are associated with dissimilarity measures to achieve the retrieval task. Then, developed writer retrieval systems are combined through SVM classifier. Experiments are conducted on ICDAR-2017 dataset which contains historical handwritten documents. The results obtained highlight the robustness of CNN features and the combination stage as well.
2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020
Developing robust signature verification systems is one of the most attracting topics in the hand... more Developing robust signature verification systems is one of the most attracting topics in the handwriting recognition field. In fact, dealing with signature spoofing requires the use of robust features that help to distinguish authentic signatures from the forged ones. Presently, we propose a multiscale fusion of two histogram-based features to perform signatures description. Precisely, we introduce the Local Difference Feature as new descriptor that is fused with the Histogram Of Templates. These features are calculated on a multiscale neighborhood to highlight pixels distribution within the signature shape. The verification stage is achieved by using SVM classifier. Performance assessment is carried out on GPDS-300 and MCYT-75 datasets. Results in terms of average error rates evince the robustness of the proposed features, which outperform various state of the art methods.
Computational Intelligence and Its Applications, 2018
Verifying the authenticity of handwritten signatures is required in various current life domains,... more Verifying the authenticity of handwritten signatures is required in various current life domains, notably with official contracts, banking or financial transactions. Therefore, in this paper a novel histogrambased descriptor and an improved classification of the bio-inspired Artificial Immune Recognition System (AIRS) are proposed for handwritten signature verification. Precisely, the Histogram Of Templates (HOT) is introduced to characterize the most widespread orientations of local strokes in handwritten signatures, while the combination of AIRS and SVM is proposed to achieve the verification task. Usually, using the k Nearest Neighbor rule, a questioned signature is classified by computing dissimilarities with respect to all AIRS outputs. In this work, using these dissimilarities, a second round of training is achieved by the SVM classifier to further improve the discrimination power. In comparison with existing methods, the experiments on two widely-used datasets show the potential and the effectiveness of the proposed system.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights An open handwritten identification system is proposed based on one-class classifier. Two methods for feature generation are proposed based on the Curvelet transform. A scheme using Fuzzy Integral (FI) is proposed for combining individual systems. A new density estimation is proposed to better adapt FI for one-class ensemble. A design framework is proposed for parameter-independent open identification system.
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