This paper presents a new computational backend model that supports Arabic document information retrieval (ADIR) as a dataset and OCR services. Therefore, different services that support document analysis, retrieving, processing including dataset preparation, and recognition will be discussed. Consequently, ADIR services provide general functions of the Arabic OCR to compose many other services in the OCR domain. Furthermore, the proposed work can provide accessing different methods of document layout analysis with a platform where they can share and handle such methods (services) without any setup requirements. One of the used datasets composed from 16,800 Arabic letters written by 60 writers. Each writer wrote each letter from Alif to Ya 10 times in two forms. The forms were scanned at 300 DPI resolution and are segmented in two sets: training set with 13,440 letters for 48 images per class label, and testing set with 3,360 letters to 120 images per class label Convolutional neural network (CNN) is used and adapted for Arabic handwritten letters classification. In an experimental test, we showed that our results outperform 100% classification accuracy rate on testing images. Therefore, the ADIR services provide a “service description”, which includes an interface and a server's URL. The interface allows communication process between clients and services. Although, in this article we evaluate IR results and compared them with respect to corrected equivalent.

Analytical based approaches in Optical Character Recognition (OCR) systems can endure a significant amount of segmentation errors, especially when dealing with cursive languages such as the Arabic language with frequent overlapping between characters. Holistic based approaches that consider whole words as single units were introduced as an effective approach to avoid such segmentation errors. Still the main challenge for these approaches is their computation complexity, especially when dealing with large vocabulary applications. In this paper, we introduce a computationally efficient, holistic Arabic OCR system. A lexicon reduction approach based on clustering similar shaped words is used to reduce recognition time. Using global word level Discrete Cosine Transform (DCT) based features in combination with local block based features, our proposed approach managed to generalize for new font sizes that were not included in the training data. Evaluation results for the approach using different test sets from modern and historical Arabic books are promising compared with state of art Arabic OCR systems.

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.

For centuries, libraries worldwide have preserved ancient manuscripts due to their immense historical and cultural value. However, over time, both natural and human-made factors have led to the degradation of many ancient Arabic manuscripts, causing the loss of significant information, such as authorship, titles, or subjects, rendering them as unknown manuscripts. Although catalog cards attached to these manuscripts might contain some of the missing details, these cards have degraded significantly in quality over the decades within libraries. This paper presents a framework for identifying these unknown ancient Arabic manuscripts by processing the catalog cards associated with them. Given the challenges posed by the degradation of these cards, simple optical character recognition (OCR) is often insufficient. The proposed framework uses deep learning architecture to identify unknown manuscripts within a collection of ancient Arabic documents. This involves locating, extracting, and classifying the text from these catalog cards, along with implementing processes for region-of-interest identification, rotation correction, feature extraction, and classification. The results demonstrate the effectiveness of the proposed method, achieving an accuracy rate of 92.5%, compared to 83.5% with classical image classification and 81.5% with OCR alone.