Offline Text-Independent Writer Identification Using a Codebook with Structural Features

Ahmed, Bashar Q; Hassan, Yaser F; Elsayed, Ashraf

doi:10.2139/ssrn.4129751

Cited by 1 publication

(1 citation statement)

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“…In the field of biometrics, studies on individual handwriting can be categorized into two types: writer identification [3] and writer verification [4]. In writer identification, the task involves identifying the author of an unknown handwriting sample from a known set of writer classes., while in writer verification, the task is to accept or reject a text image sample to verify whether or not it is from a given writer.…”

Section: Introductionmentioning

confidence: 99%

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

Paul

Dutta

Sarkar

et al. 2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%