GR-RNN: Global-context residual recurrent neural networks for writer identification. (September 2021)

• Record Type: Journal Article
• Authors: He, Sheng; Schomaker, Lambert
• Is Part Of: Pattern recognition. Volume 117(2021)
• Publication Date: 2021-09
• Subjects: Writer identification -- Recurrent neural network -- Residual network -- Local and global features; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.107975
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We present a global-context residual recurrent neural network for writer identification. The proposed method integrates the global context and the information of a sequence of local fragments by recurrent neural networks. We evaluate the proposed method on four public datasets, showing state-of-the-art performance. Abstract: This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features. The global-context information is extracted from the tail of the neural network by a global average pooling step. The sequence of local and fragment-based features is extracted from a low-level deep feature map which contains subtle information about the handwriting style. The spatial relationship between the sequence of fragments is modeled by the recurrent neural network (RNN) to strengthen the discriminative ability of the local fragment features. We leverage the complementary information between the global-context and local fragments, resulting in the proposed global-context residual recurrent neural network (GR-RNN) method. The proposed method is evaluated on four public data sets and experimental results demonstrate that it can provide state-of-the-art performance. In addition, the neural networks trained on gray-scale images provide better results than neural networks trained on binarized and contour images, indicating that texture information plays an important role for writer identification. The source code is available: https://github.com/shengfly/writer-identification .