Duplicate Document Detection using Machine Learning
Project Context
Table of Contents
Due to the current state of technological development, businesses and individuals are investing in computerized systems to facilitate quick and effective daily operations. Different tasks are now quick, simple, and convenient to complete thanks to technology. This is an example of how the researcher for the capstone project “Duplicate Document Detection using Machine Learning” adopts the idea of technology transforming conventional methods into cutting-edge ones. With the help of this technology, enterprises and individuals will no longer struggle to delete redundant documents, which requires a lot of work and wastes time. The system uses sophisticated detection so that when a document is duplicated; it may be quickly and easily identified, informing users of the situation. Apart from benefiting users in terms of alerting them via the detection feature, it also benefits users in that it saves space on their desktop or other devices. Additionally, it would be simple for them to find the detected documents and they would notice right away, giving them time to delete. This would prevent the papers from being buried in the folders and causing the space provided to fill up.
The fact that certain documents are duplicated results in a lot of wasted and full storage. Aside from that, finding and deleting duplicate papers is a waste of time and effort. The goal of this technique is to conceal such items while making it easy to find such documents. It is created utilizing advanced machine learning, which is sophisticated enough to complete a task quickly and effectively.
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Objectives of the Study
General Objectives: The primary objective of this work is to create and put into practice a highly effective and efficient duplicate document detection system utilizing machine learning.
Specifically, the study aims to:
- To develop technology capable of quickly and accurately locating duplicated papers.
- to create a tool that makes it simple for people and businesses to find superfluous documents and delete them.
- To create a platform that would make it easier and more automated to find duplicate and pointless documents.
- To alert the user when duplicate documents exist, enabling them to have a large amount of storage space on their particular devices.
- To create a solution that enables organization members to track duplicate documents electronically.
Significance of the Study
The success of the project is significant to the following individuals or groups:
Companies/Organizations: The system’s success is seen as advantageous by various businesses or groups. They will be helped by the system to efficiently keep and remove necessary and pointless documents. The system will also make document tracking simple, quick, and comfortable because it will be clever and intelligent enough to detect duplicate documents. They would be able to identify duplicate documents with ease thanks to these clever features, and their files would be well-organized and tidy.
Individuals/employees: they can use the technology on an individual basis, which makes their work simple, quick, and efficient. Their desktop or PC would run more quickly and effectively as a result. The necessary documentation are easily accessible to authorized personnel. Additionally, they can easily track and identify documents in the system for storage and quick retrieval when committing revisions.
Researchers. Their experience as developers may have been enhanced by the travel they took while carrying out the project. The project’s development will better their expertise and research abilities.
Future Researchers. They can make their own version of the Document Tracking System using the research as a guide.
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What is Machine Learning?
Machine learning is a subfield of artificial intelligence that deals with the design and development of algorithms that can learn from and make predictions on data. Machine learning algorithms have been used in a variety of areas, including computer vision, speech recognition, and natural language processing.
Machine learning is based on the idea that machines can learn from data, without being explicitly programmed. Instead, the machine learning algorithm “learns” how to predict the correct answer by using a set of training data (examples of what is expected to happen in the future). The machine learning algorithm can then be used to make predictions on new data sets, without needing to be explicitly programmed with the rules for how this data should be analyzed.
One of the key features of machine learning is its ability to learn from data sets that are very different from each other. This is why machine learning is often used to analyze data sets that are difficult for humans to understand, such as financial data or scientific data sets.
Near-Duplicate Detection Method
A near-duplicate is a copy of a document that is very similar to another document. The near-duplicate detection method is used to identify these documents.
There are several key points to this method:
- The first step is to create a fingerprint for each document. This fingerprint is a summary of the document’s unique features.
- The next step is to compare the fingerprints of the documents. If they are very similar, then the documents are likely copies.
- Finally, if the fingerprints are similar, then it is likely that the documents were created by someone who was familiar with the original document. This means that the documents are likely near-duplicates.
The near-duplicate detection method is a useful tool for detecting copies of documents. It can help to identify similar documents and determine if they are copies or not. This method can be used in a number of different situations, such as copyright infringement or document fraud. By using this method, organizations can protect their intellectual property and prevent fraud from happening. Overall, the near-duplicate detection method is a useful tool that can be used to protect organizations and their data.
Conclusion
The study was carried out by the researchers to evaluate the present approach of locating and tracking each document in an organization. The existing approach of tracking, detecting, and locating duplicate documents is time- and effort-consuming, according to the researchers’ detailed examination and investigation. The researchers used this information to create the “Duplicate Document Detection using Machine Learning” platform, which enables both an individual and an organization to find such pointless and duplicated documents. The study’s findings demonstrated that the created system satisfied the needs and specifications of the respondents and the intended users. The vast majority of respondents and the system’s intended users have recognized its potential.
The researchers came to the conclusion that the system created is a useful and effective tool for locating duplicate papers within an organization. The platform was intelligently designed by the researcher to make it simple for users to recognize when a document has been saved more than once, alerting them to the duplicated file and instructing them to immediately remove it. The system’s presence allows for more storage, which organizes the data and makes it easier for individuals to remove and store papers. The technology that has been designed will increase productivity and lower expenses while processing documents.
Recommendations
The study’s findings led the researchers to suggest that the system be put in place. Because it can provide the target consumers with efficiency and dependability, the developed project comes highly recommended. The researchers contend that the intended users must be familiar with the system’s operation. They also recommend that businesses and organizations employ the method to boost workplace productivity. The system should definitely be put in place because it will simplify all transactions involving documents. The created system is advised because it will make it simple for employees to track, retrieve, edit, and update various documents.
Summary
A technology called duplicate document detection using machine learning enables both organizations and individuals to identify duplicate files and documents. This will be used to let users know when there are duplicate copies of certain documents, saving them space and time when they stack the files and allowing them to delete them. This sophisticated technology can identify duplicate documents, saving businesses and individuals the time and effort of fighting over the deletion of these tasks. The system uses sophisticated detection so that when a document is duplicated, it may be quickly and easily identified, informing users of the situation. Apart from benefiting users in terms of alerting them via the detection feature, it also benefits users in that it saves space on their desktop or other devices. After a comprehensive investigation, it was determined that the platform did evaluate users’ worries about deleting and tracking a document’s duplicate. Consequently, because of its advantages and benefits to the system, it was advised by the researchers.
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