Plagiarism detection in Arabic texts
The system compares Arabic texts at the level of meaning, not letters, detecting plagiarism even after paraphrasing — exactly where off-the-shelf tools fail.
The problem
Available plagiarism tools were designed for English and fail in Arabic because of diacritics, hamza variations, and the many forms of a single word. Students rephrase with synonyms and get through undetected, and a teacher reading dozens of assignments doesn't notice the similarity. The comparison needs to happen at the level of meaning, not letters — something literal tools don't do.
How it works
- CleaningThe text is processed by normalizing hamzas and diacritics and reducing words to their roots.
- RepresentationEach paragraph is converted into a numeric representation that carries its meaning, not its letters.
- ComparisonThe system compares the representations with each other and with the source database.
- Paraphrase detectionHigh similarity in meaning with different wording indicates paraphrasing.
- ReportThe report shows the similarity percentage and identifies the matching paragraphs and their sources.
- Group comparisonAll the section's assignments are compared with each other to detect copying between students.
Project features
- Designed specifically for Arabic, in a market lacking effective ready-made tools.
- It detects paraphrasing, not just literal copying.
- Group comparison reveals copying between students — a practical feature for teachers.
- The report identifies the paragraph and its source, and the final decision stays with the teacher.
- It has real users: universities, schools, and academic journals.
Parts
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Expected challenges
These are the main obstacles to expect during the build, laid out clearly so you know them before you start.
- Legitimate quotation looks like plagiarism in form, and they're hard to tell apart.
- Arabic has many word forms, writing styles, and hamza variations; cleaning is a big part of the work.
- Building a large enough Arabic source database for comparison.
- A wrong accusation causes serious harm to the student, so the system only suggests and never passes judgment.
- Group comparison grows quadratically with the number of assignments.
The consultation gives you a solution for each one.
Expected committee questions
Prepare your answers in advance: these questions come up often in defenses of similar projects.
- How does the system compare texts at the level of meaning, and which model is used?
- What challenges are specific to Arabic, such as diacritics, morphology, and synonyms?
- How was detection accuracy measured, and where did the plagiarized texts used in testing come from?
- How does the system differ from tools like Turnitin in handling Arabic texts?
Students' experiences
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Need solutions to the challenges and a complete technical path?
In the consultation we give you a solution for each challenge above, plus a practical build plan from the first step to the last: technologies, libraries, wiring diagram, timeline, and tips for the defense.
- Consultation session
- PDF with solutions and a plan
- 20 د.أ فقط