A feature extraction technique for online handwriting recognition
- Authors: Verma, Brijesh , Lu, Jenny , Ghosh, Moumita , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Conference paper
- Relation: Paper presented at 2004 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary : 25th July, 2004
- Full Text: false
- Reviewed:
- Description: The paper presents a feature extraction technique for online handwriting recognition. The technique incorporates many characteristics of handwritten characters based on structural, directional and zoning information and combines them to create a single global feature vector. The technique is independent to character size and it can extract features from the raw data without resizing. Using the proposed technique and a Neural Network based classifier, many experiments were conducted on UNIPEN benchmark database. The recognition rates are 98.2% for digits, 91.2% for uppercase and 91.4% for lowercase.
- Description: E1
- Description: 2003000868
A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier
- Authors: Ghosh, Moumita , Ghosh, Ranadhir , Verma, Brijesh
- Date: 2004
- Type: Text , Journal article
- Relation: International Journal of Pattern Recognition and Artificial Intelligence Vol. 18, no. 7 (Nov 2004), p. 1267-1283
- Full Text: false
- Reviewed:
- Description: In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing- Author
- Description: C1
- Description: 2003000867
A fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems
- Authors: Verma, Brijesh , Kulkarni, Siddhivinayak
- Date: 2004
- Type: Text , Journal article
- Relation: Journal of Applied Soft Computing Vol. 5, no. 1 (2004), p. 119-130
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- Reviewed:
- Description: This paper presents a fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. The presented approach uses fuzzy logic to interpret queries expressed in natural language such as mostly red, many green, few red for colour feature. Tamura feature is used to represent the texture of an image in the database. A term set on each Tamura feature is generated using a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and Tamura feature values. A fusion of multiple queries is incorporated into the proposed approach. The performance of the technique was evaluated on Brodatz texture benchmark database and it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted using neurofuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that fuzzy-neural approach has significantly improved the performance of CBIR system.
- Description: C1
- Description: 2003002798
A novel approach for structural feature extraction : Contour vs. direction
- Authors: Verma, Brijesh , Blumenstein, Michael , Ghosh, Moumita
- Date: 2004
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 25, no. 9 (2004), p. 975-988
- Full Text: false
- Reviewed:
- Description: The paper presents a novel approach for extracting structural features from segmented cursive handwriting. The proposed approach is based on the contour code and stroke direction. The contour code feature utilises the rate of change of slope along the contour profile in addition to other properties such as the ascender and descender count, start point and end point. The direction feature identifies individual line segments or strokes from the character's outer boundary or thinned representation and highlights each character's pertinent direction information. Each feature is investigated employing a benchmark database and the experimental results using the proposed contour code based structural feature are very promising. A comparative evaluation with the directional feature and existing transition feature is included. © 2004 Elsevier B.V. All rights reserved.
- Description: C1
- Description: 2003002951
Combination strategies for finding optimal neural network architecture and weights
- Authors: Verma, Brijesh , Ghosh, Ranadhir
- Date: 2004
- Type: Text , Book chapter
- Relation: Neural information processing : Research and development Chapter p. 294-319
- Full Text: false
- Description: The chapter presents a novel neural learning methodology by using different combination strategies for finding architecture and weights. The methodology combines evolutionary algorithms with direct/matrix solution methods such as Gram-Schmidt, singular value decomposition, etc., to achieve optimal weights for hidden and output layers. The proposed method uses evolutionary algorithms in the first layer and the least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons and weights using hierarchical combination strategies. The chapter explores all different facets of the proposed method in terms of classification accuracy, convergence property, generalization ability, time and memory complexity. The learning methodology has been tested using many benchmark databases such as XOR, 10 bit odd parity, handwriting characters from CEDAR, breast cancer and heart disease from UCI machine learning repository. The experimental results, detailed discussion and analysis are included in the chapter.
- Description: 2003004097
A hierarchical method for finding optimal architecture and weights using evolutionary least square based learning
- Authors: Ghosh, Ranadhir , Verma, Brijesh
- Date: 2003
- Type: Text , Journal article
- Relation: International Journal of Neural Systems Vol. 13, no. 1 (2003), p. 13-24
- Full Text: false
- Reviewed:
- Description: In this paper, we present a novel approach of implementing a combination methodology to find appropriate neural network architecture and weights using an evolutionary least square based algorithm (GALS).1 This paper focuses on aspects such as the heuristics of updating weights using an evolutionary least square based algorithm, finding the number of hidden neurons for a two layer feed forward neural network, the stopping criterion for the algorithm and finally some comparisons of the results with other existing methods for searching optimal or near optimal solution in the multidimensional complex search space comprising the architecture and the weight variables. We explain how the weight updating algorithm using evolutionary least square based approach can be combined with the growing architecture model to find the optimum number of hidden neurons. We also discuss the issues of finding a probabilistic solution space as a starting point for the least square method and address the problems involving fitness breaking. We apply the proposed approach to XOR problem, 10 bit odd parity problem and many real-world benchmark data sets such as handwriting data set from CEDAR, breast cancer and heart disease data sets from UCI ML repository. The comparative results based on classification accuracy and the time complexity are discussed.
- Description: 2003004100