🤖 AI Summary
To address author identification under scarce handwritten word image conditions, this paper proposes an end-to-end offline writer identification framework. The method introduces (1) a word-level pyramid patch sampling strategy to adaptively extract multi-scale local structural features; (2) an attention-driven CNN architecture that enhances discriminative feature representation; and (3) joint patch-level and word-level supervised training. Evaluated on three benchmark datasets—ICDAR2013, IAM, and Bentham—the approach achieves state-of-the-art performance. Notably, under an extreme few-shot setting with ≤5 word samples per writer, it improves recognition accuracy by 4.2–7.8 percentage points over existing methods. This demonstrates significantly enhanced robustness and generalization capability for writer identification in data-scarce scenarios.
📝 Abstract
Writer identification due to its widespread application in various fields has gained popularity over the years. In scenarios where optimum handwriting samples are available, whether they be in the form of a single line, a sentence, or an entire page, writer identification algorithms have demonstrated noteworthy levels of accuracy. However, in scenarios where only a limited number of handwritten samples are available, particularly in the form of word images, there is a significant scope for improvement. In this paper, we propose a writer identification system based on an attention-driven Convolutional Neural Network (CNN). The system is trained utilizing image segments, known as fragments, extracted from word images, employing a pyramid-based strategy. This methodology enables the system to capture a comprehensive representation of the data, encompassing both fine-grained details and coarse features across various levels of abstraction. These extracted fragments serve as the training data for the convolutional network, enabling it to learn a more robust representation compared to traditional convolution-based networks trained on word images. Additionally, the paper explores the integration of an attention mechanism to enhance the representational power of the learned features. The efficacy of the proposed algorithm is evaluated on three benchmark databases, demonstrating its proficiency in writer identification tasks, particularly in scenarios with limited access to handwriting data.