Impact of data augmentation on handwriting characters recognition

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dc.contributor.authorSaidi, Zahra
dc.contributor.authorFortas, Roufeida
dc.contributor.authorKhellas, Kenza
dc.date.accessioned2023-11-27T20:10:44Z
dc.date.available2023-11-27T20:10:44Z
dc.date.issued2023
dc.description.abstractHandwriting recognition involves converting handwritten text into digital form. It plays a pivotal role in various applications. Although recent advancements in optical character recognition have been significant, challenges continue to persist. Fortunately, the emergence of deep neural networks presents powerful solutions to address these obstacles. Motivated by the need for improved recognition accuracy in real-world scenarios, we aim to investigate the impact of data augmentation techniques on the performance of deep learning models for handwriting recognition tasks. The ensuing chapters of this dissertation delve into the fundamental principles of handwriting recognition, deep learning methodologies tailored for handwriting recognition, data augmentation techniques, and present comprehensive experimental analysis and results. Employing a Convolutional Neural Network (CNN) model trained on two distinct handwriting datasets, this study conducts a comprehensive experimental analysis. The results underscore the efficacy of data augmentation in enhancing recognition accuracy, highlighting its potential to bolster the performance of deep learning models in handwriting recognition tasks.
dc.identifier.urihttp://dspace.univ-oeb.dz:4000/handle/123456789/17050
dc.language.isoen
dc.publisherUniversity of Oum El Bouaghi
dc.titleImpact of data augmentation on handwriting characters recognition
dc.title.alternativecomparative Study
dc.typeOther
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