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 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