Deep adaptive learning for writer identification based on single handwritten word images. (April 2019)

• Record Type: Journal Article
• Authors: He, Sheng; Schomaker, Lambert
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 64
• Page End: 74
• Publication Date: 2019-04
• Subjects: Writer identification -- Deep adaptive learning -- Handwritten word attributes -- Multi-task learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.11.003
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We present a method for writer identification based on single handwritten word images. High-level features learned from the auxiliary task are transferred to the task of writer identification in a multi-task learning framework. We evaluate the deep adaptive learning with three auxiliary tasks: word content, word length and character attribute recognition. Abstract: There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.