A Comprehensive and Comparative Study of Handwriting Recognition System
Citations Over Time
Abstract
Handwriting recognition has made a significant impact in the digital world. Handwriting recognition is used in a wide range of applications such as digitizing documents, recognizing postal addresses, interpreting forms and so on. These systems can convert handwritten text into a digital format which can be easily processed by machines. Handwriting recognition is a sustainable technology that is typically based on deep learning techniques, which involve training a model on a large dataset of handwritten samples. There are various types of handwriting recognition systems such as online systems that analyze handwriting in real-time and offline systems that analyze pre-recorded images of handwriting. The technique selection depends upon the application usage and dataset size. There are also hybrid systems that combine both character-level and word-level recognition. In this paper, 422 Scopus papers are analyzed and a comprehensive and comparative analysis of different techniques are discussed.
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