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Personality traits are of paramount importance in identifying the human’s behavior. They represent a useful information source for forensic control, recruitment profiling, medical symptoms, and other applications. Personality traits are identified through various physical aspects, including sense, honesty, and other emotions. These aspects can be revealed through handwritten features. Since handwriting is unique for everyone, its identification process is not as straight forward as it appears; rather it involves efficient tools for extraction and classification of features. The process has been the subject of various research works. However, results reported remain unsatisfactory due to mainly dissimilarities in handwriting. In this paper, we present an approach of recognition of personality traits based on textural features extracted from handwritten samples. Experiments are carried out using artificial neural networks and the TxPI-u database. Results deliver a significant recognition rate which endorses its effectiveness against similar works.

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