A survey of handwritten character recognition with mnist and emnist
This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset
for handwritten digit recognition. This dataset has been extensively used to validate novel …
for handwritten digit recognition. This dataset has been extensively used to validate novel …
Texture feature extraction methods: A survey
A Humeau-Heurtier - IEEE access, 2019 - ieeexplore.ieee.org
Texture analysis is used in a very broad range of fields and applications, from texture
classification (eg, for remote sensing) to segmentation (eg, in biomedical imaging), passing …
classification (eg, for remote sensing) to segmentation (eg, in biomedical imaging), passing …
A novel un-supervised burst time dependent plasticity learning approach for biologically pattern recognition networks
Bio-inspired computing is an appropriate platform for developing artificial intelligent
machines based on the behavioral and functional principles of the brain. Bio-inspired …
machines based on the behavioral and functional principles of the brain. Bio-inspired …
Hybridizing evolutionary computation and deep neural networks: an approach to handwriting recognition using committees and transfer learning
Neuroevolution is the field of study that uses evolutionary computation in order to optimize
certain aspect of the design of neural networks, most often its topology and …
certain aspect of the design of neural networks, most often its topology and …
Recognizing intertwined patterns using a network of spiking pattern recognition platforms
Artificial intelligence computing adapted from biology is a suitable platform for the
development of intelligent machines by imitating the functional mechanisms of the nervous …
development of intelligent machines by imitating the functional mechanisms of the nervous …
[HTML][HTML] Performance analysis of hybrid deep learning framework using a vision transformer and convolutional neural network for handwritten digit recognition
Digitization created a demand for highly efficient handwritten document recognition systems.
A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an …
A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an …
Hand detection by two-level segmentation with double-tracking and gesture recognition using deep-features
Vision-based hand gesture recognition involves a visual analysis of handshape, position
and/or movement. Most of the previous approaches require complex gesture representation …
and/or movement. Most of the previous approaches require complex gesture representation …
Convolutional neural network-based ensemble methods to recognize Bangla handwritten character
In this era of advancements in deep learning, an autonomous system that recognizes
handwritten characters and texts can be eventually integrated with the software to provide …
handwritten characters and texts can be eventually integrated with the software to provide …
[PDF][PDF] An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification.
G Abosamra, H Oqaibi - Computers, Materials & Continua, 2021 - cdn.techscience.cn
Even though much advancements have been achieved with regards to the recognition of
handwritten characters, researchers still face difficulties with the handwritten character …
handwritten characters, researchers still face difficulties with the handwritten character …
Handwritten character recognition using knn and svm based classifier over feature vector from autoencoder
Optical character recognition system is a necessity for the field of man-machine interaction.
Handwritten character recognition is a subset of OCR technique by which computer …
Handwritten character recognition is a subset of OCR technique by which computer …