Novel Feature Extraction Methods to Detect Age from Handwriting.

Age detection from handwritten documents is a crucial research area in many disciplines such as forensic analysis and medical diagnosis. Furthermore, this task is challenging due to the high similarity and overlap between individuals' handwriting. The performance of the document recognition and analysis systems, depends on the extracted features from handwritten documents, which can be a challenging task as this depends on extracting the most relevant information from row text. In this paper, a set of age-related features suggested by a graphologist, to detect the age of the writers, have been proposed. These features include irregularity in slant, irregularity in pen pressure, irregularity in textlines, and the percentage of black and white pixels. Support Vector Machines (SVM) classifier has been used to train, validate and test the proposed approach on two different datasets: the FSHS and the Khatt dataset. The proposed method has achieved a classification rate of 71% when applied to FSHS dataset. Meanwhile, our method outperformed state-of-arts methods when applied to the Khatt dataset with a classification rate of 65.2%. Currently, these are the best rates in this field.