GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification

TL;DR

This paper introduces GR-RNN, a neural network that combines global and local handwriting features with recurrent modeling to improve writer identification accuracy, demonstrating state-of-the-art results on multiple datasets.

Contribution

The paper proposes a novel end-to-end neural network architecture that integrates global-context and local fragment features with RNNs for enhanced writer identification.

Findings

GR-RNN achieves state-of-the-art performance on four datasets.

Texture information from gray-scale images improves identification accuracy.

Global-context and local fragment features complement each other effectively.

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 will be available: \url{https://github.com/shengfly/writer-identification}.