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

Arabic handwriting recognition is a crucial area of computer vision research.Still, its complexity, diverse writing styles, and overlapping words have led to a lack of published research in this field.This paper suggests two new models to recognize handwritten Arabic words, depending on the Faster Region-Convolution Neural Network (Faster R-CNN).These models used two pre-trained networks during the feature extraction phase: The Visual Geometry Group-16 (VGG-16) network and the Residual Network (ResNet50) network.Models are independently trained and tested on two datasets: The Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT) dataset and the KFUPM Handwritten Arabic Text (KHATT) dataset.Test results showed that the proposed models give excellent results compared to others.The results of VGG16 and ResNet50 with the IFN/ENIT dataset reached accuracy rates of 92% and 100%, respectively.Meanwhile, the accuracy of the KHATT dataset reached 99.4% and 98% with VGG16 and ResNet50, respectively.