Farhan Mohamed

Improving Right to Left Cursive Handwritten Text Recognition in Historical Manuscripts Using Learnable Edge Features and Channel Attention

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 collections.