Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Algebraic Description of Curve Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel feature extraction method and hybrid tree classification for handwritten numeral recognition
Pattern Recognition Letters
Structural Decomposition and description of Printed and Handwritten Characters
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Fuzzy model based recognition of handwritten numerals
Pattern Recognition
Handwriting Recognition Algorithm in Different Languages: Survey
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Expert Systems with Applications: An International Journal
A statistical-topological feature combination for recognition of handwritten numerals
Applied Soft Computing
Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Previous handwritten numeral recognition algorithms applied structural classification to extract geometric primitives that characterize each image, and then utilized artificial intelligence methods, like neural network or fuzzy memberships, to classify the images. We propose a handwritten numeral recognition methodology based on simplified structural classification, by using a much smaller set of primitive types, and fuzzy memberships. More specifically, based on three kinds of feature points, we first extract five kinds of primitive segments for each image. A fuzzy membership function is then used to estimate the likelihood of these primitives being close to the two vertical boundaries of the image. Finally, a tree-like classifier based on the extracted feature points, primitives and fuzzy memberships is applied to classify the numerals. With our system, handwritten numerals in NIST Special Database 19 are recognized with correct rate between 87.33% and 88.72%.