Writer Identification using a Deep Neural Network
Jun Chu, Sargur N. Srihari · 2014
Most work on automatic writer identification relies on handwriting features defined by humans[6, 4]. These features correspond to basic units such as letters and words of text. Instead of relying on human-defined features, we consider here the determination of writing similarity using automatically determined word-level features learnt by a deep neural network. We generalize the problem of writer identification to the definition of a content-irrelevant handwriting similarity. Our method first takes whether two words were written by the same person as a discriminative label for word-level feature training. Then, based on word-level features, we define writing similarity between passages. This similarity not only shows the distinction between writing styles of different people, but also the development of style of the same person. Performance with several hidden layers in the neural network are evaluated. The method is applied to determine how a person's writing style changes with time considering a children's writing dataset. The children's handwriting data are annually collected. They were written by children of 2nd, 3rd or 4th grade. Results are given with a whole passage (50 words) of writing over one-year change. As a comparison, similar experiments on a small amount of data using conventional generative model are also given.