Font and function word identification in document recognition
Computer Vision and Image Understanding
Optical Font Recognition Using Typographical Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of touching characters using an MLP
Pattern Recognition Letters
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Font Recognition Based on Global Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Empirical evaluation of dissimilarity measures for color and texture
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Font Recognition and Contextual Processing for More Accurate Text Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Evaluation of distance metrics for recognition based on non-negative matrix factorization
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
FyFont: find-your-font in large font databases
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
| Hi-index | 0.00 |
This paper proposes an approach to font classification for document image understanding using non-negative matrix factorization (NMF). The basic idea of the proposed method is based on that the characteristics of each font are derived from parts of the individual characters in each font rather than holistic textures. Spatial localities, parts composing of font images, are automatically extracted using NMF. These parts are used as features representing each font. In the experimental results, the distribution of features and the appropriateness of use of the characteristics specifying each font are investigated. Add to that, the proposed method is compared with the method based on principal component analysis (PCA), in which various distance metrics are tested in the feature space. It expects that the proposed method will increase the performance of optical character recognition (OCR) systems or document indexing and retrieval systems if such systems adopt the proposed font classifier as a preprocessor.