Zone based relative density feature extraction algorithm for unconstrained handwritten numeral recognition
- Title
- Zone based relative density feature extraction algorithm for unconstrained handwritten numeral recognition
- Creator
- Saravanan K.N.; Anitha R.
- Description
- The recognition of handwritten characters and numerals has been a challenging problem among the researchers for few decades. This paper proposes a relative density feature extraction algorithm for recognizing unconstrained single connected handwritten numerals independent of the languages. The proposed method consists of four phases, namely, image enhancement (dilation), representation (zone based), feature extraction (relative density) and recognition (minimum distance classifier). The handwritten numerals must be enhanced with dilation, in order to connect the broken digits. After enhancement, the dilated binary images can be represented as a mid-point aspect ratio class interval values. There can be M * N zones and subsequently there would be 2M*N relational density exist using mid-point aspect ratio class interval values. In order to minimize the number of features, a subset of W relative densities has been extracted from the binary image since the relative density is too large to be handled efficiently. The minimum distance classifier technique has been used to recognize the given numerals. The proposed algorithm would be an alternative to recognize the handwritten numerals for recognizing unconstrained single connected handwritten numerals. The method sounds promising with a recognition rate of 92.8567%. 2005 - 2014 JATIT & LLS. All rights reserved.
- Source
- Journal of Theoretical and Applied Information Technology, Vol-64, No. 1, pp. 123-130.
- Date
- 2014-01-01
- Publisher
- Asian Research Publishing Network (ARPN)
- Subject
- Feature extraction; Handwritten numeral recognition; K-NN classifier; Minimum distance classifier; Zoning
- Coverage
- Saravanan K.N., Christ University, Bangalore, Karnataka, India; Anitha R., Muthayammal Engg. College, Rasipuram, Tamilnadu, India
- Rights
- Restricted Access
- Relation
- ISSN: 19928645
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Saravanan K.N.; Anitha R., “Zone based relative density feature extraction algorithm for unconstrained handwritten numeral recognition,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/17296.