An Arabic Manuscript Regions Detection, Recognition and Its Applications for OCRing

Abstract

The problem of Region of Interest (RoI) in document layout analysis and document recognition has recently become an essential topic in OCRing systems. Arabic manuscript layout analysis and OCRing recognition using language detection, document category, and RoI with Keras and TensorFlow are terms of the state-of-the-art that should be investigated. This article investigates the problem of Arabic manuscript recognition problems with respect to in OCRing-based recognition. A new framework architecture, which integrates Fast Gradient Sign Method (FGSM) using Keras and TensorFlow with adversarial image generation during training procedure is proposed. Also, the article tries to improve the OCRing accuracy of the image enhancement, alignment, layout analysis, and recognition using deep learning in multilingual system. RoIs detections will be performed using a custom trained deep learning model using bounding box regression with Keras and TensorFlow. This topic investigates an extension of Page Segmentation Method (PSM) to enhance OCRing parameter modes and enhances Arabic OCRing system accuracy from reinforcement strategy. Therefore, the article achieves a significant improvement of OCRing results due to the three parameters: language identification, document category, and RoI types (Table, Title, Paragraph, figure, and list). This model is based on “region proposal algorithm” as a basis of CNN object detectors to find the number of the RoIs. Therefore, the proposed framework performs three distinctive tasks: (1) CNN architecture for adversarial training, (2) an implementation of the FGSM with Keras and TensorFlow, and (3) an adversarial training script implementation with the CNN and the FGSM method. The experiments on Arabic manuscript dataset including Arabic text, English/Arabic documents, and Latin digits’ datasets, demonstrate the accuracy of the proposed method. Moreover, the proposed framework performs well and succeeded in defending against adversarial attacks or adversarial images. The experimental results on our collected dataset illustrate the novelty of our proposed framework over the other existing PSM methods to be extended and updated to improve the quality of the OCRing system. The results show that the influence of PSM after expanding using the RoI types, language ID, and document/manuscript category can improve the OCRing accuracy. Also, the experimental results show significant performance by the new framework model with accuracy reached to 99% compared to relative methods.