AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
Handwriting recognition comparison is a critical yet challenging task in financial, commercial, and exam cheating prevention applications. However, the drawbacks of existing methods are that they compare the whole sentence or the entire signature with multiple characters susceptible to data noise. Their accuracy fluctu...
These findings demonstrate that explicit character localization provides a robust, data-efficient alternative for Arabic handwritten text recognition in low-resource settings.
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...
Online writer verification with dynamic handwriting signals is still difficult, and it has been especially under-studied forcomplex Southeast Asian scripts like Khmer. This work tackles online, text-independent, word-level Khmer writer verification as apairwise decision problem: given two handwritten word samples, deci...
The Attention-Enhanced CNN-KAN (A-CNN-KAN), an innovative hybrid model that combines Convolutional Neural Networks for effective feature extraction, Kolmogorov-Arnold Networks for flexible non-linear pattern modeling, and a spatial attention mechanism to emphasize salient features, is presented.
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.
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