Arabic Documents Layout Analysis (ADLA) using Fine-tuned Faster RCN

At present, there is a massive interest in document digitization, image searching, and natural language processing models, using different types of models. The first step in applying any type of processing like the image to text converting, is layout analysis, which is the paper's interest field. The problem in layout analysis comes in Arabic language, where there is a well-noticed gap for research in this field. The main limitations of the existed research are common, the dataset size, where in its return, gives a not very accurate result. In this paper, we are using two distinct types of Arabic language datasets. We propose a tuned model for layout analysis for Arabic printed and early printed documents using Faster RCNN (ADLA). The proposed model is based on tuning Faster Region-based Convolutional Neural Network (RCNN) model to match our two datasets, with different regions of interest (RoI). For evaluation, we compared the proposed model with two distinct existing models (LABA & FFRA). The F1 score result for our proposed model exceeds the LABA model with 99.4%, whereas the LABA model has 90.5%. Our model exceeds the FFRA model with 99.59% accuracy, whereas the FFRA model got 99.83% accuracy result.