ReLU to Enhance MDLSTM for Offline Arabic Handwriting Recognition

Using Faster R-CNN to Detect and Recognize Arabic Handwritten Words

“…3 Recently, researchers have endeavoured to develop methods for recognizing Arabic handwritten words using the IFN/ENIT. In 2021, Maalej and Kherallah [12] suggested an offline Arabic handwriting recognizer that used a Multi-Dimensional Long Short-Term Memory Network (MDLSTM) and Rectified Linear Units (ReLUs) to fix issues with vanishing gradients and dropout to avoid overfitting. Evaluated on the IFN/ENIT database, the systems achieved a label error rate of 11.40%.…”

“…MDLSTM, for instance, is susceptible to the vanishing gradient problem. The authors in [12] incorporated the Rectified Linear Unit (ReLU) to address this challenge. To reduce overfitting-related issues, [3,12,13] used regularization techniques like dropout.…”

“…The authors in [12] incorporated the Rectified Linear Unit (ReLU) to address this challenge. To reduce overfitting-related issues, [3,12,13] used regularization techniques like dropout. During the feature extraction step, [1,3] employed a sliding window approach to feeding data into the CNN, which involves scanning the original image horizontally using a sliding window that moves from right to left.…”