Plagiarism detection is an important concept in content verification academic integrity and software development. In this blog post we will build a simple plagiarism checker using Python that compares two text files and calculates how similar they are based on common words.
This project is beginner friendly and helps in understanding file handling sets and basic text processing in Python.
What Is Plagiarism Detection
Plagiarism detection is the process of identifying similarities between two pieces of text. In this example we focus on detecting direct word overlap rather than paraphrasing or semantic similarity. While this approach is basic it forms the foundation for more advanced techniques used in real world plagiarism tools.
Approach Used in This Project
The plagiarism checker follows a straightforward approach
First both text files are read and converted to lowercase to ensure fair comparison.
Next the text is split into individual words.
The words are stored in sets which automatically remove duplicate words.
Common words between the two files are identified using set intersection.
Finally a similarity percentage is calculated based on the proportion of shared words.
Explanation of the Logic
The key idea behind the similarity calculation is to measure how much of the first document appears in the second one. This is done by dividing the number of common words by the total number of unique words in the first file and converting the result into a percentage.
This method answers the question how much of file one overlaps with file two.
Why Sets Are Used
Sets are an efficient data structure in Python that store unique values. By converting word lists into sets we remove duplicates automatically and make it easy to compare common elements between two documents.
This also improves performance when working with larger text files.
Limitations of This Method
This plagiarism checker has some limitations
It only detects exact word matches.
It does not detect paraphrased or rewritten text.
It does not consider sentence structure or word frequency.
Despite these limitations it is a great starting point for understanding plagiarism detection concepts.
Possible Improvements
This project can be extended in many ways
Using sentence based comparison
Applying TF IDF and cosine similarity
Building a web interface using Streamlit or Flask
Comparing multiple files at once
These enhancements make the checker more powerful and closer to real world systems.
Conclusion
This simple plagiarism checker demonstrates how basic Python concepts like file handling sets and string processing can be combined to solve a real problem. It is an excellent beginner project and a strong addition to a Python portfolio.