Handwritten Digit Recognition
23 papers with code • 1 benchmarks • 5 datasets
MNIST
NumtaDB
MNIST-MIX
DigiLeTs
Latest papers with no code
Cloud-Based Deep Learning: End-To-End Full-Stack Handwritten Digit Recognition
Herein, we present Stratus, an end-to-end full-stack deep learning application deployed on the cloud.
Bengali Handwritten Digit Recognition using CNN with Explainable AI
In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits.
Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition
Moreover, fewer studies have been done on Bangla handwritten digit recognition (BHwDR).
Two Decades of Bengali Handwritten Digit Recognition: A Survey
This paper will also serve as a compendium for researchers interested in the science behind offline BHDR, instigating the exploration of newer avenues of relevant research that may further lead to better offline recognition of Bengali handwritten digits in different application areas.
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition
The recognition accuracy of the HOG+SVM method on the NumtaDB, CMARTdb, Ekush and BDRW datasets reached 93. 32%, 98. 08%, 95. 68% and 89. 68%, respectively as well as we compared the model performance with recent state-of-art methods.
Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses
We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplication.
Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique
The testing part contains the testing of a new dataset . This part always comes after the part of the training . At first one has to teach the child how to recognize the character . Then one has to take the test whether he has given right answer or not.
Efficient Majority Voting in Digital Hardware
In this work, we present a novel architecture that allows obtaining a majority decision in a number of clock cycles that is logarithmic in the number of inputs.
Efficient Learning of Pinball TWSVM using Privileged Information and its applications
Thus, in order to get the advantage from an expert knowledge and to reduce the sensitivity towards the noise, in this paper, we propose privileged information based Twin Pinball Support Vector Machine classifier (Pin-TWSVMPI) where expert's knowledge is in the form of privileged information.
Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks
However, the resistive change behavior in this regime suffers many fluctuations and is particularly challenging to model, especially in a way compatible with tools used for simulating deep learning.
