TL;DR
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.
This study focuses on enhancing handwritten Devanagari character recognition using deep learning models, specifically fine-tuned Convolutional Neural Networks (CNNs), combined with hybrid mathematical methods for image enhancement, proposing a fuzzy-enabled Power-Law transformation for image enhancement.
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...
A deep learning-based handwritten character recognition system that leverages Convolutional Neural Networks for automatic feature extraction and classification and highlights the effectiveness of deep learning techniques in enhancing recognition performance and reducing classification errors compared to conventional ma...
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 effectiveness of CNN–BiLSTM–CTC sequence modeling for offline recognition of handwritten Kazakh words in the Latin script using a convolutional recurrent neural network is demonstrated.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
