Biometrics has been an active research area for a long time aiming at automatic identity recognition based on individual physiological or behavioral characteristics. Personal identification based on handwriting is a kind of behavioral biometric identification approach. Each person has his individual writing style and the handwritings are easy to obtain. Handwriting based personal identification has a wide variety of potential applications, from security, forensics, financial activities to archeology. For this reason, much research has touched on this field, but most of them rested on signature verification, which has the disadvantage in that the identification content is fixed and limited, making it prone to forgery. This master thesis proposes several efficient methods for writer identification through deep analysis of the problem nature. The thesis is organized as follows. Chapter 1 introduces some concepts about handwriting identification (HI) and the significance, application background, difficulties and evaluation criteria of HI technology. Also the most popular methods of HI are presented in this chapter. Chapter 2 describes the data collection manner and mainly focuses on the preprocessing processes of the on-line and off-line handwriting data, which include infliction point and false pen-lift removal, character normalization and handwriting image slant correction method. Chapter 3 realizes the text-independent HI based on texture analysis, and compare the performance of them. A novel algorithm is presented for writer identification in chapter 4. Principal . Component Analysis is applied to the gray-scale handwriting images to find a set of individual words which best characterize a person's handwriting style and have maximal difference from other people style. During identification, we only need to utilize a set of individual characteristic words for comparison, instead of comparing the whole handwriting text to identify the writers. Chapter 5 gives another text-independent on-line HI method, which mainly utilizes the visual shape features of character and dynamic features for handwriting identification. Chapter 6 summary the whole paper and give a prospect of the development of HI.
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