Abstract
Principal component analysis (PCA) has been a major field of study in image compression, coding technique, or pattern recognition, particularly for classification and feature subset selection. Based on its success in these domains, character recognition methods using PCA have attracted considerable attention in recent years. In this paper, we propose a novel scheme for gray-scale handwritten character recognition based on principal of training set are projected onto the subspaces defined by their most important eigenvectors. Here, the significant eigenvectors of each class are chosen as those with the largest associated eigenvalues. These eigenvectors can be thought of as a set of feature vectors, that is, principal features. In this paper, we consider the minimum error subspace classifier for classification. It is a discriminant function derived from the PCA. We discriminate an unknown test character during the recognition phase by projection and classification. The recognition is performed by projecting a test image onto the subspace defined by the dominant eigenvectors of each class and then choosing the class corresponding to the subspace with the minimum error as the class of the test character. In order to verify the performance of the proposed scheme for gray-scale handwritten character recognition, experiments with the IPTP CDROM1 database have been carried out. Of the 12,000 samples available on this CD, 9,000 and 3,000 have been sued for training and testing, respectively. In this paper, we investigated the influence of the number eigencharacters used to define the subspace as well as the number of training characters for each character. Experimental results reveal that the proposed scheme based on principal features has advantages over other character recognition approaches in its speed and simplicity, learning capacity, and insensitivity to variations in the handwritten character images.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Publisher | Society of Photo-Optical Instrumentation Engineers |
| Pages | 40-49 |
| Number of pages | 10 |
| Volume | 3027 |
| ISBN (Print) | 0819424382 |
| Publication status | Published - 1997 |
| Externally published | Yes |
| Event | Document Recognition IV - San Jose, CA, USA Duration: 1997 Feb 12 → 1997 Feb 13 |
Other
| Other | Document Recognition IV |
|---|---|
| City | San Jose, CA, USA |
| Period | 97/2/12 → 97/2/13 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Gray-scale handwritten character recognition based on principal features. / Park, Hee Seon; Kim, Sang Yup; Lee, Seong Whan.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 3027 Society of Photo-Optical Instrumentation Engineers, 1997. p. 40-49.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Gray-scale handwritten character recognition based on principal features
AU - Park, Hee Seon
AU - Kim, Sang Yup
AU - Lee, Seong Whan
PY - 1997
Y1 - 1997
N2 - Principal component analysis (PCA) has been a major field of study in image compression, coding technique, or pattern recognition, particularly for classification and feature subset selection. Based on its success in these domains, character recognition methods using PCA have attracted considerable attention in recent years. In this paper, we propose a novel scheme for gray-scale handwritten character recognition based on principal of training set are projected onto the subspaces defined by their most important eigenvectors. Here, the significant eigenvectors of each class are chosen as those with the largest associated eigenvalues. These eigenvectors can be thought of as a set of feature vectors, that is, principal features. In this paper, we consider the minimum error subspace classifier for classification. It is a discriminant function derived from the PCA. We discriminate an unknown test character during the recognition phase by projection and classification. The recognition is performed by projecting a test image onto the subspace defined by the dominant eigenvectors of each class and then choosing the class corresponding to the subspace with the minimum error as the class of the test character. In order to verify the performance of the proposed scheme for gray-scale handwritten character recognition, experiments with the IPTP CDROM1 database have been carried out. Of the 12,000 samples available on this CD, 9,000 and 3,000 have been sued for training and testing, respectively. In this paper, we investigated the influence of the number eigencharacters used to define the subspace as well as the number of training characters for each character. Experimental results reveal that the proposed scheme based on principal features has advantages over other character recognition approaches in its speed and simplicity, learning capacity, and insensitivity to variations in the handwritten character images.
AB - Principal component analysis (PCA) has been a major field of study in image compression, coding technique, or pattern recognition, particularly for classification and feature subset selection. Based on its success in these domains, character recognition methods using PCA have attracted considerable attention in recent years. In this paper, we propose a novel scheme for gray-scale handwritten character recognition based on principal of training set are projected onto the subspaces defined by their most important eigenvectors. Here, the significant eigenvectors of each class are chosen as those with the largest associated eigenvalues. These eigenvectors can be thought of as a set of feature vectors, that is, principal features. In this paper, we consider the minimum error subspace classifier for classification. It is a discriminant function derived from the PCA. We discriminate an unknown test character during the recognition phase by projection and classification. The recognition is performed by projecting a test image onto the subspace defined by the dominant eigenvectors of each class and then choosing the class corresponding to the subspace with the minimum error as the class of the test character. In order to verify the performance of the proposed scheme for gray-scale handwritten character recognition, experiments with the IPTP CDROM1 database have been carried out. Of the 12,000 samples available on this CD, 9,000 and 3,000 have been sued for training and testing, respectively. In this paper, we investigated the influence of the number eigencharacters used to define the subspace as well as the number of training characters for each character. Experimental results reveal that the proposed scheme based on principal features has advantages over other character recognition approaches in its speed and simplicity, learning capacity, and insensitivity to variations in the handwritten character images.
UR - http://www.scopus.com/inward/record.url?scp=0031357627&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031357627&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0819424382
VL - 3027
SP - 40
EP - 49
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - Society of Photo-Optical Instrumentation Engineers
ER -