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.
The proposed model is a 2-channel CNNs model with multiple spatial-attention convolutional layers that operates at the character level, enabling finer-grained analysis with reduced sensitivity to spacing and alignment variations.
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...
KVerifyID, a hybrid dual-stream Siamese network that learns from both a grayscale image rendering of each word and its pen-trajectory sequence, enables robust Khmer writer verification, making it promising for digital authentication and forensicscreening.
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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