  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.
