An integrated approach towards improvement of writer verification performance using page level, line level and word level data on Bangla handwritten document

Handwriting analysis presents a formidable challenge, especially when dealing with languages like Bangla that lack extensive resources. Previous research on Bangla handwritten document recognition has focused on various levels, including page, block, line, and word analysis. However, the improvement of page-level recognition in this context remains an unexplored area. This paper aims to fill this gap by proposing an integrated approach that combines techniques at the page level, line level, and word level for writer verification. We have developed a novel method that significantly improves the performance of page-level writer verification, utilizing our newly created dataset, JUDVLP-BLWVdb. By utilizing the widely recognized ensemble technique of majority voting, we combine three classifiers (Support Vector Machine, Multilayer Perceptron, and Simple Logistic). Our proposed method achieves a remarkable increase in page level writer verification accuracy, improving by it nearly 12% to 97.62% across 101 different writers. Furthermore, we compare the results of our dataset with four state-of-the-art writer verification approaches. Additionally, we explore the application of deep learning-based approaches, including VGG16, ResNet34, and AlexNet, using our dataset.