Classification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods

Abstract

The writing process, in which feelings and thoughts are expressed in writing, differs from person to person. Handwriting samples, which are very easy to obtain, are frequently used to identify individuals because they are biometric data. Today, with human-machine interaction increasing by the day, machine learning algorithms are frequently used in offline handwriting identification. Within the scope of this study, a dataset was created from 3250 handwritten images of 65 people. We tried to classify collected handwriting samples according to person and gender. In the classification made for person and gender recognition, feature extraction was done using 32 different transfer learning algorithms in the Python program. For person and gender estimation, the classification process was carried out using the random forest algorithm. 28 different classification algorithms were used, with DenseNet169 yielding the most successful results, and the data were classified in terms of person and gender. As a result, the highest success rates obtained in person and gender classification were 92.46% and 92.77%, respectively.

Investigation of signature biometric evidence using deep learning for forecasting personality

“…The two kinds of handwritten datasets used in their work were handwritten words and handwritten structure, both of which were gathered using a pen tablet. The research (Agduk and Aydemir 2022) was to figure out from handwritten specimen images, which vary from individual to individual, the gender of the individual who penned the words as well as their identity. The study (Saleema and Thampi 2020) was to determine whether it is possible to create biometric templates from user profiles that satisfy the reliability and uniqueness requirements for biometric identification.…”