Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR)
Given the ubiquity of handwritten documents in human transactions, Optical Character
Recognition (OCR) of documents have invaluable practical worth. Optical character …
Recognition (OCR) of documents have invaluable practical worth. Optical character …
Improved handwritten digit recognition using convolutional neural networks (CNN)
Traditional systems of handwriting recognition have relied on handcrafted features and a
large amount of prior knowledge. Training an Optical character recognition (OCR) system …
large amount of prior knowledge. Training an Optical character recognition (OCR) system …
Neural networks architectures design, and applications: A review
Artificial Neural Networks (ANNs) are modern computing methods that have been used
extensively in solving many complicated problems in the physical world. The attractiveness …
extensively in solving many complicated problems in the physical world. The attractiveness …
[HTML][HTML] Futures of artificial intelligence through technology readiness levels
Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However,
the main unanswered questions about this foreseen transformation are its depth, breadth …
the main unanswered questions about this foreseen transformation are its depth, breadth …
Human face recognition based on convolutional neural network and augmented dataset
P Lu, B Song, L Xu - Systems Science & Control Engineering, 2021 - Taylor & Francis
To deal with the issue of human face recognition on small original dataset, a new approach
combining convolutional neural network (CNN) with augmented dataset is developed in this …
combining convolutional neural network (CNN) with augmented dataset is developed in this …
Learn to augment: Joint data augmentation and network optimization for text recognition
Handwritten text and scene text suffer from various shapes and distorted patterns. Thus
training a robust recognition model requires a large amount of data to cover diversity as …
training a robust recognition model requires a large amount of data to cover diversity as …
Convolutional-neural-network-based handwritten character recognition: an approach with massive multisource data
Neural networks have made big strides in image classification. Convolutional neural
networks (CNN) work successfully to run neural networks on direct images. Handwritten …
networks (CNN) work successfully to run neural networks on direct images. Handwritten …
Handwritten character recognition using convolutional neural network
Handwritten character recognition (HCR) is the detection of characters from images,
documents and other sources and changes them in machine-readable shape for further …
documents and other sources and changes them in machine-readable shape for further …
[HTML][HTML] Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process
This paper presents an approach for image classification based on an ensemble of
convolutional neural networks and the application to a real case study of an industrial …
convolutional neural networks and the application to a real case study of an industrial …
Cross lingual handwritten character recognition using long short term memory network with aid of elephant herding optimization algorithm
In the recent decades, the handwritten character recognition is still a challenging process in
the pattern recognition field. The handwritten digits and characters are not always of the …
the pattern recognition field. The handwritten digits and characters are not always of the …