AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The results showed model’s robustness against the type of salt-and-pepper noise, moderate behavior against the type of speckle and sensitivity to high Gaussian variance noise, and the range of noise variance and density is the best level for practical operation.
This paper deals with offline writer verification on complex handwriting patterns, where handcrafted feature PDFs are hybridized with auto-derived CNN features and fed into a Siamese neural network for writer verification.
A new approach is presented here that integrates traditional handcrafted texture features such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM) with deep learning models with deep learning models.
An edge-aware line-level HTR framework that extends a CNN-Transformer baseline with a learnable edge-extraction channel and Squeeze-and-Excitation channel attention and shows that combining learnable structural cues with channel-wise attention has improved robustness for degradation-prone historical manuscript collecti...
The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to accurately identify handwritten numerical digits from input images by using image processing techniques and a Convolutional Neural Network model trained on the MNIST dataset.
The efficient-KAN literature---covering Chebyshev, wavelet, and radial-basis-function variants of the original Kolmogorov-Arnold Network---has been benchmarked almost entirely on clean data. We show that this choice conceals a large capability difference between architectures: ChebyKAN's test MSE (evaluated against cle...
A compact convolutional network for 46-class DHCD Devanagari recognition and reached 99.73%, the highest reported at 15.6x smaller than prior state-of-the-art, effectively reaching the saturation point.
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