Design and simulation of handwritten detection via generative adversarial networks and convolutional neural network

Sasipriyaa, N.; Natesan, Prathiba; Mohana, Rajni; Gothai, E.; Venu, K.; Mohanapriya, S.

doi:10.1016/j.matpr.2021.05.024

Cited by 6 publications

(1 citation statement)

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“…They evaluated their method using the ICDAR 2013 Chinese handwriting competition database and found that the new writer adaptation DNN-HMM achieved a higher recognition rate than the writer-independent DNN-HMM. Sasipriyaa et al (2021) in their study on English handwritten characters, researchers offered two models: GAN for generating English handwritten character recognition and the CNN model. The study's findings suggested that the produced English character pictures improved the performance of character classifiers.…”

Section: Other Methods For Handwritten Recognitionmentioning

confidence: 99%

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

Ali¹,

Abdulrazzaq²

2023

Journal of Computer Science

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Deep learning and deep neural networks, particularly ConvolutionalNeural Networks (CNNs), are rapidly growing areas of machine learning and are currently the primary tools used for image analysis and classification applications. Handwriting recognition involves using computer algorithms and software to interpret and recognize handwritten text and drawings and has various applications such as automated handwriting analysis, document digitization, and handwriting-based user interfaces. Many deep learning models have been applied in the field of handwriting recognition and various datasets have been used to evaluate new computer vision techniques. This article provides an overview of the current state-of-the-art approaches and contributions to handwriting recognition using different datasets. Furthermore, the paper explains the most commonly used algorithms for recognizing handwritten characters, words, and numbers. Compares them based on their accuracy. This study covered different aspects and methods of machine learning and Deep Learning (DL) for handwritten recognition that showed different achievements for each.

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Section: Other Methods For Handwritten Recognitionmentioning

confidence: 99%