User opinions and feedback on mobile applications are crucial for application developers, offering insights into issues like bugs, popular features, and enhancement requests. Given the vast number of feedback for each app, it is impractical for developers to manually extract valuable information. To better understand and analyze user opinions, developers can benefit from automatic sentiment analysis and classification of app reviews. Existing research has primarily focused on reviews written in English, with some studies addressing sentiment analysis of Arabic reviews but overlooking the classification task. Given the widespread use and complexity of Arabic compared to English, our work investigates both sentiment analysis and classification of Arabic app reviews. We introduce the AURA ( A pp U ser R eview in A rabic) dataset. AURA dataset has two versions: AURA-Sentiment with 29,700 labeled reviews for sentiment analysis, and AURA-Classification with 2,900 labeled reviews for classification. Using these datasets, we applied deep learning (DL) and natural language processing (NLP) techniques for analyzing and classifying Arabic app reviews. Leveraging the MarBert model, we achieved an F1-score of 0.89 for sentiment analysis and an F1-score of 0.62 for a four-class classification problem. Our findings provide valuable insights and suggest directions for future research.

In this paper we will present our investigations related to contextual modeling for HMM-based handwritten Arabic text recognition. We will, first, discuss the justifications and the need for contextual modeling for handwritten Arabic text recognition. Next, we will discuss the issues related to contextual modeling for Arabic text recognition. Finally, we will present our novel class-based contextual modeling for HMM-based handwritten Arabic text recognition. Experiment results on word recognition tasks show improvements in word recognition rates when compared to using standard contextual HMMs. Moreover, the recognizers are significantly more compact as compared to the standard contextual HMM systems.

Handwritten text recognition is an active research area in pattern recognition. One of the prerequisites of setting up a handwritten text recognizer is to train them using, mostly, large amounts of labeled training data. In the current paper we report our work on handwritten text recognition using no handwritten training set. We investigate different approaches including, computer generated text in different typefaces as training data, unsupervised adaptation, and using recognition hypothesis on the test sets as training data. Results from handwritten Arabic word recognition task show that the approach is promising with good recognition rates.

In this paper, we present a multi-stage approach to handwritten Arabic text recognition using HMM where we separate the Arabic text image into core components and diacritics and recognize them separately using two separate HMM recognition systems. In the next stage, we combine the scores from both recognizers to make a final word hypothesis. This approach leads to huge reduction in the number of HMM models that need to be trained. Experiments conducted on a word recognition task using a publicly available benchmark database show the effectiveness of the technique. We achieve state-of-the-art results in addition to a compact model set for the recognition system.

The Arabic script allows the replacement of certain character sequences by more compact forms called ligatures. Such ligatures lack a systematic analysis despite their importance in Arabic text recognition research. In this paper, we present analysis of ligatures and compile a comprehensive list of Arabic ligatures. Then, we perform recognition experiments on a benchmark database to show the impact of ligatures annotation in Arabic databases. Finally, we propose several guidelines for the design of representative and compact Arabic databases from the aspect of ligatures.

Sub-character HMM models for Arabic text recognition allow sharing of common patterns between different position-dependent shape forms of an Arabic character as well as between different characters. The number of HMMs gets reduced considerably while still capturing the variations in shape patterns. This results in a compact, efficient, and robust recognizer with reduced model set. In the current paper we are presenting our recent improvements in sub-character HMM modeling for Arabic text recognition where we use special 'connector' and 'space' models. Additionally we investigated contextual sub-characters HMMs for text recognition. We also present multi-stream contextual sub-character HMMs where the features calculated from a sliding window frame form one stream and its derivative features are part of the second stream. We report state-of-the-art results on the IFN/ENIT (benchmark) database of handwritten Arabic text and the recognition rate of 85.12% on sets outperforms previously published results.

Hidden Markov Model (HMM) is one of the most widely used classifier for text recognition. In this paper we are presenting novel sub-character HMM models for Arabic text recognition. Modeling at sub-character level allows sharing of common patterns between different contextual forms of Arabic characters as well as between different characters. The number of HMMs gets reduced considerably while still capturing the variations in shape patterns. This results in a compact and efficient recognizer with reduced model set and is expected to be more robust to the imbalance in data distribution. Experimental results using the sub-character model based recognition of handwritten Arabic text as well printed Arabic text are reported.

In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) after completion of the collection of 1000 handwritten forms written by 1000 writers from different countries. It is composed of an image database containing images of the written text at 200, 300, and 600 dpi resolutions, a manually verified ground truth database that contains meta-data describing the written text at the page, paragraph, and line levels. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. Preliminary experiments on Arabic handwritten text recognition are conducted using sample data from the database and the results are reported. The database will be made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, etc.