A Novel Feature Analysis Approach Improve to Chinese Signature Recognition

• Main Authors: Chin-Wei Wu, 吳至偉
• Other Authors: 方志鵬
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/ms8s8s

碩士 === 國立臺北科技大學 === 電機工程系研究所 === 95 === In the online character recognition, the trajectories of pen tip movements are recoded and analyzed to identify the writer. Online character recognition is able to yield higher accuracy than offline recognitions because of temporal stroke and spatial shapes information. It can also correct the errors and improve the accuracies with a large volume of writer information. The applications of online recognitions include text entries used for form filling and message compositions, personal digital assistants (PDA), computer-aided educations, handwritten document retrievals, and etc. Another useful application is the signature verification that checks whether a specific writer generates a personal handwriting signature. In the thesis, we implement a novel Chinese signature recognition technique, which is originally developed for the classification of the remotely sensed hyperspectral images recently. Since a huge volume of features is collected to improve the classification accuracy in our proposed GME method, the repeated and redundant features are expected to be larger. In order to improvement performance, we proposed a novel greedy modular eigenspace (GME) method to reduce feature dimensions. In this thesis, the performance of GME is evaluated in comparison with the conventional feature extraction methods. The approach is designed as a feature extractor for Chinese signature recognitions to simplify these redundant features. It can extract the simplest and the most efficient signature feature modules collected from each signature that includes real-stroke and virtual-stroke features. A new developed feature scale uniformity transformation (FUST) is also performed to fuse the most similar signature features from different writers. The performance of the proposed GME/FUST method is evaluated by a signature database. The experimental results demonstrate the proposed method is an effective scheme not only for the feature extractions of the signature recognitions but also for computational reductions complexity of the signature databases.