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This paper provides a literature survey about Automatic Speech Recognition (ASR) systems for learning Arabic language and Al-Quran Recitation. The growth in communication technologies and AI (specially Machine learning and Deep learning) led researchers in ASR field to thinking of and developing ASR systems which mimic humans in their understand of natural speech and recognition. One of the most important applications in ASR is natural language processing (NLP). Arabic language is one of these languages. ASR systems which developed for Arabic language help Arabs and non-Arabs in learning Arabic language and so Al-Quran recitation and memorization in proper way according to recitation rules (Tajweed). This paper concentrate on ASR systems in general, challenges, PROS, CONS, Arabic language ASR systems and challenges faced them and finally Al-Quran recitation verification systems.

Social media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important role in identifying appropriate emergency responses to a particular disaster. In this paper, we proposed a disaster taxonomy of emergency response and used the same taxonomy with an emergency response pipeline together with deep-learning-based image classification and object identification algorithms to automate the emergency response decision-making process. We used the card sorting method to validate the completeness and correctness of the disaster taxonomy. We also used VGG-16 and You Only Look Once (YOLO) algorithms to analyze disaster-related images and identify disaster types and relevant cues (such as objects that appeared in those images). Furthermore, using decision tables and applied analytic hierarchy processes (AHP), we aligned the intermediate outputs to map a disaster-related image into the disaster taxonomy and determine an appropriate type of emergency response for a given disaster. The proposed approach has been validated using Earthquake, Hurricane, and Typhoon as use cases. The results show that 96% of images were categorized correctly on disaster taxonomy using YOLOv4. The accuracy can be further improved using an incremental training approach. Due to the use of cloud-based deep learning algorithms in image analysis, our approach can potentially be useful to real-time crisis management. The algorithms along with the proposed emergency response pipeline can be further enhanced with other spatiotemporal features extracted from multimedia information posted on social media.

Extraction of news text captions aims at a digital understanding of what is happening in a specific region during a certain period that helps in better communication between different nations because we can easily translate the plain text from one language to another. Moving text captions causes blurry effects that are a significant cause of text quality impairments in the news channels. Most of the existing text caption detection models do not address this problem in a way that captures the different dynamic motion of captions, gathers a full news story among several frames in the sequence, resolves the blurring effect of text motion, offers a language-independent model, or provides it as an end-to-end solution for the community to use. We process the frames coming in sequence and extract edge features using either the Hough transform or our color-based technique. We verify text existence using a Convolutional Neural Network (CNN) text detection pre-trained model. We analyze the caption motion status using hybrid pre-trained Recurrent Neural Network (RNN) of Long Short-Term Memory (LSTM) type model and correlation-based model. In case the motion is determined to be horizontal rotation, there are two problems. First, it means that text keeps rotating with no stop resulting in a high blurring effect that affects the text quality and consequently resulting in low character recognition accuracy. Second, there are successive news stories which are separated by the channel logo or long spaces. We managed to solve the first problem by deblurring the text image using either Bicubic Spline Interpolation (BSI) technique or the Denoising Autoencoder Neural Network (DANN). We solved the second problem using a Point Feature Matching (PFM) technique to match the existing channel logo with the channels’ logo database (ground truth). We evaluate our framework using Abbyy® SDK as a standalone tool used for text recognition supporting different languages.

A new technique for text detection inside a complex graphical background, its extraction, and enhancement to be easily recognized using the optical character recognition (OCR). The technique uses a deep neural network for feature extraction and classifying the text as containing text or not. An error handling and correction (EHC) technique is used to resolve classification errors. A multiple frame integration (MFI) algorithm is introduced to extract the graphical text from its background. Text enhancement is done by adjusting the contrast, minimize noise, and increasing the pixels resolution. A standalone software Component-Off-The-Shelf (COTS) is used to recognize the text characters and qualify the system performance. Generalization for multilingual text is done with the proposed solution. A newly created dataset containing videos with different languages is collected for this purpose to be used as a benchmark. A new HMVGG16 convolutional neural network (CNN) is used for frame classification as text containing or non-text containing, has accuracy equals to 98%. The introduced system weighted average caption extraction accuracy equals to 96.15%. The correctly detected characters (CDC) average recognition accuracy using the Abbyy SDK OCR engine equals 97.75%.