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Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic.
An Efficient Single Chord-based Accumulation Technique (SCA) to Detect More Reliable Corners
The Chord-to-Point Distance Accumulation (CPDA) detector is recognized as the contour-based corner detector producing the lowest localization error while localizing corners in an image.
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks
Increased accuracy in predictive models for handwritten character recognition will open up new frontiers for optical character recognition.
Optical Character Recognition
Optical Character Recognition (OCR)
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Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
In this study, a prediction system for the disease of diabetes is pre-sented where the issue of overfitting is minimized by using the dropout method.
Chord Angle Deviation using Tangent (CADT), an Efficient and Robust Contour-based Corner Detector
We have mentioned a number of popular contour-based corner detectors in our paper.
Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks
Handwritten character recognition is an active area of research with applications in numerous fields.
