We describe several systems for identifying short samples of Arabic or SwissGerman dialects, which were prepared for the shared task of the 2017 DSLWorkshop (Zampieri et al., 2017). The Arabic data comprises both text and acoustic files, and our best run combined both. The SwissGerman data is text-only. Coincidently, our best runs achieved a accuracy of nearly 63% on both the Swiss-German and A...

Most text mining techniques have been proposed only for English text, and even here, most research has been conducted on specific texts related to special contexts within the English language, such as politics, medicine and crime. In contrast, although Arabic is a widely spoken language, few mining tools have been developed to process Arabic text, and some Arabic domains have not been studied a...

We describe a SMS-based information system called CATS, which allows posting and searching through free Arabic text using Information Extraction (IE) technology. We discuss the challenges of applying IE technology for unedited real Arabic text. In addition, we describe the structure of this system and our approach to produce an open robust system capable of including more sub domains with the m...

This paper describes PLANET, a recognition method to be applied on Arabic documents with complex structures allowing incremental learning in an interactive environment. The classification is driven by artificial neural nets each one being specialized in a document model. The first prototype of PLANET has been tested on five different phases of newspaper image analysis: thread recognition, frame...

We present a free, Java-based library named “AraNLP” that covers various Arabic text preprocessing tools. Although a good number of tools for processing Arabic text already exist, integration and compatibility problems continually occur. AraNLP is an attempt to gather most of the vital Arabic text preprocessing tools into one library that can be accessed easily by integrating or accurately adap...

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition syste...

The contribution of this work relates to the field of Arabic text-based document analysis for the detection of plagiarism. This analysis will be carried out according to the triadic computation model of document similarity. The authors propose a hybrid segmentation prototype for Arabic text-based documents that links different processing steps in order to generate the similarity rate between th...

One of the issues that arise in the text steganography is the capacity of hiding secret bit. Focusing in Arabic text steganography we propose a sharp-edges method to encounter the issue. This new method will hide the secret bits in the sharp-edges for each character in the Arabic text document. The main processes involved are identifying sharp-edges in the cover-text, secret message preparation...

Automatic text summarization has become important due to the rapid growth of information texts since it is very difficult for human beings to manually summarize large documents of texts. A full understanding of the document is essential to form an ideal summary. However, achieving full understanding is either difficult or impossible for computers. Therefore, selecting important sentences from t...

More and more, interest in the way data is displayed on screen has increased, especially with the increase in the number of people using e-text for learning purposes. So, this requires more focus on factors that affect screen legibility. Text display factors, such as font size, line length and font type, have an impact on reading online. Two font types [Arabic Traditional and Simplified Arabic]...