Use of MKL as symbol classifier for Gujarati character recognition
09 Jun 2010-pp 255-262
TL;DR: The MKL based classification is proposed, where the MKL is used for learning optimal combination of different features for classification and the comparison results in 1-Vs-1 framework and using KNN classifier are presented.
Abstract: The present work is part of ongoing effort to improve the performance of Gujarati character recognition. In the recent advancement in kernel methods, the novel concept of multiple kernel learning(MKL) has given improved results for many problems. In this paper, we present novel application of MKL for Gujarati character recognition. We have applied three different feature representations for symbols obtained after zone wise segmentation of Gujarati text. The MKL based classification is proposed, where the MKL is used for learning optimal combination of different features for classification. In addition MKL based classification results for different features is also presented. The multiclass classification is performed in Decision DAG framework. The comparison results in 1-Vs-1 framework and using KNN classifier is also presented. The experiments have shown substantial improvement in earlier results.
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TL;DR: The exhaustive experimental evaluation of the proposed framework on a collection of documents belonging to Devanagari, Bengali and English scripts has yielded encouraging results.
Abstract: In this paper, we propose a novel feature representation for binary patterns by exploiting the object shape information. Initial evaluation of the representation is performed for Bengali and Gujarati script character classification. The extension of the representation for word images is presented subsequently. The proposed feature representation in combination with distance-based hashing is applied for defining novel word image-based document image indexing and retrieval framework. The concept of hierarchical hashing is utilized to reduce the retrieval time complexity. In addition, with the objective of reduction in the size of hashing data structure, the concept of multi-probe hashing is extended for binary mapping functions. The exhaustive experimental evaluation of the proposed framework on a collection of documents belonging to Devanagari, Bengali and English scripts has yielded encouraging results.
16 citations
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TL;DR: This paper presents a Structural feature based method for classification of printed Gujarati characters that deals with varied sizes, font styles, and stoke widths.
Abstract: This paper presents a Structural feature based method for classification of printed Gujarati characters. The ability to provide incremental definition of characters in terms of its native components makes the proposal unique and versatile. It deals with varied sizes, font styles, and stoke widths. The features are validated on subset of machine printed Gujarati characters using a simple rule based classifier and the initial results are encouraging.
8 citations
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TL;DR: A novel binary multiple kernel learning-based classification architecture for applications including characters/primitives and symbols including such problems for fast and efficient performance is demonstrated.
Abstract: The paper presents a novel framework for large class, binary pattern classification problem by learning-based combination of multiple features. In particular, class of binary patterns including characters/primitives and symbols has been considered in the scope of this work. We demonstrate novel binary multiple kernel learning-based classification architecture for applications including such problems for fast and efficient performance. The character/primitive classification problem primarily concentrates on Gujarati and Bangla character recognition from the analytical and experimental context. A novel feature representation scheme for symbols images is introduced containing the necessary elastic and non-elastic deformation invariance properties. The experimental efficacy of proposed framework for symbol classification has been demonstrated on two public data sets.
7 citations
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05 Feb 2013
TL;DR: A hybrid approach for recognition of Gujarati handwritten numerals using neural networks as classifier and achieved a good recognition rate for noisy numerals is presented.
Abstract: The handwriting recognition is the scheme of converting text symbolized in the spatial form of graphical symbols into its figurative depiction. Handwritten characters have been the most accredited technique of collecting, storing and transmitting information all the way through the centuries. To give the proper ability to the machine it requires studying the image-form of data which forms a special pattern to be interpreted. Designing and building machines that can recognize patterns remains one of the thrust areas in the field of computer sciences. A lot of work has been done in this field, but still the problem is not answered in its full density. A good text recognizer has many commercial and practical applications, e.g. from finding data in digitized book to computerization of any organization, like post office, which involve manual task of interpreting text. In this paper, we have presented a hybrid approach for recognition of Gujarati handwritten numerals using neural networks as classifier and achieved a good recognition rate for noisy numerals.
4 citations
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TL;DR: A novel word image based document indexing scheme by combination of string matching and hashing is presented for two document image collections belonging to Devanagari and Bengali script.
Abstract: We present a novel word image based document indexing scheme by combination of string matching and hashing The word image representation is defined by string codes obtained by unsupervised learning over graphical primitives The indexing framework is defined by distance based hashing function which does the object projection to hash space by preserving their distances We have used edit distance based string matching for defining the hashing function and for approximate nearest neighbor based retrieval The application of the proposed indexing framework is presented for two document image collections belonging to Devanagari and Bengali script
1 citations
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TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
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TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
Abstract: Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space---classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm---using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
2,362 citations
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Proceedings Article•
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TL;DR: An algorithm, DAGSVM, is presented, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG, which is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.
Abstract: We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an N-class problem, the DDAG contains N(N - 1)/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG. The DAGSVM is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.
1,837 citations
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TL;DR: The relative merits of performing local operations on ~ digitized picture in parallel or sequentially are discussed and some applications of the connected component and distance functions are presented.
Abstract: The relative merits of performing local operations on ~ digitized picture in parallel or sequentially are discussed. Sequential local operations are described which l~bel the connected components of a given subset of the picture and compute u \"distance\" from every picture element to the subset. In terms of the \"distance\" function, ~ \"skeleton\" subset is defined which, in a certain sense, minimally determines the original subset. Some applications of the connected component and distance functions are also presented.
1,642 citations
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Journal Article•
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TL;DR: It is shown that the proposed multiple kernel learning algorithm can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations, and generalize the formulation and the method to a larger class of problems, including regression and one-class classification.
Abstract: While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for SVMs, especially when used with sparse feature maps as appear for string kernels, allowing us to train a string kernel SVM on a 10 million real-world splice data set from computational biology. We integrated multiple kernel learning in our machine learning toolbox SHOGUN for which the source code is publicly available at http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun .
1,335 citations
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