Feature Extraction and Selection for Handwriting Identification: A review

Handwriting is a skill that is personal to individual [28]. The relation of character, shape and the style of writing are visually different from one to another. Handwriting identification is a process to identify or verify the authorship of a handwriting document. Asserting authorship identity based on handwritten text requires three steps: Data acquisition and preprocessing, Feature extraction, and Classification. In the first step, the handwriting images is preprocessed and normalized to perform handwriting identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. Handwriting features are writer’s characteristics to individuality. There are two different approaches to obtaining features: feature extraction and feature selection. In feature extraction the features that may have discriminating power were extracted, while in feature selection, a subset of the original set of features is selected. This paper concentrated on the state of art of feature extraction and selection approaches based on character, word, text line, and paragraph. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting identification based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.