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.
Abstract (translated)
近年来，由于其在各个领域广泛应用，手写识别因其在场景中提供最佳手写样本而受到欢迎。当最优手写样本以单个行、句子或整个页面的形式存在时，手写识别算法已经展示了相当高的准确性。然而，在仅存在有限个手写样本的情况下，尤其是在以单词图像形式存在时，改进的空间更大。在本文中，我们提出了一个基于注意力的卷积神经网络（CNN）的手写识别系统。该系统通过从单词图像中提取图像片段（称为片段），并使用金字塔策略进行训练。这种方法使系统能够捕捉数据的全局表示，包括细粒度和粗特征，跨越各种抽象层次。这些提取的片段成为卷积网络的训练数据，使其能够从基于单词图像的传统卷积网络中学到更健壮的表示。此外，本文还探讨了将注意机制集成到学到的特征中以增强其表示能力的可能性。所提出的算法的有效性在三个基准数据库上的评估表明，其在手写识别任务中的熟练程度，尤其是在手写数据访问有限的情况下。
URL
https://arxiv.org/abs/2404.07602
