Identifying duplicate document manually is simple, however, identifying duplicate documents or images using automated system with 100% accuracy is a complex process. Finding duplicate documents or images can be easily done through hashing and comparisons, again, the hashing approach has many limitations like it works best for only perfect duplicates, which is not applicable for near duplicates images or documents. We would discuss how to identify info items that convey the same (or very similar) content and identify near duplicate documents using Machine Learning (ML) in the form of Natural Language Processing (NLP).

In this blog I walk through how to identify near duplicate image or document by automated program using NLP and AI algorithms that has been integrated in the framework of an automated system using the text extraction, classification, and clustering of documents in the system to increase the performance of the comparison function. To overcome real world context of complexities of near duplicate document detection, we have implemented semantic similarity measures, to apply semantic analysis to the similarity assessment of a pair of document.

Near Duplicate Detection

I will discuss the overall approach for near duplicate document detection process in detail here.

Phase one: Optical character recognition (OCR)

Optical character recognition is the initiative for identifying duplicate documents images, OCR refers to reading and converting typed, printed, scanned or handwritten characters into machine-encoded text. OCR is a technology that we used to recognizes text among a digital images or scanned document. it is normally used to recognize text in scanned documents, however it serves several other purposes as well, OCR platforms make copies of documents like scanned receipts, bank statements, bank check, passports and other types of documentation that require to be managed further. If you’ve ever transformed a text into a PDF with a program like Adobe acrobat, then you’ve used OCR. The quality of OCR has steady improved ever since it was created unfortunately, the demands of modern enterprises have quick outstripped its growth. Corporations are starting to turn to AI-driven alternatives to boost their efficiency and extract meaning. Simply creating templates of documents is no longer enough enterprises want insights now the days as well.

Phase two: Keyword/phrase extraction.

Keyword extraction (also referred to as keyword detection or keyword analysis) is a text analysis technique that consists of automatically extracting the most important word or phrase and expressions in a text. It helps summarize the content of a text and acknowledge the main topics which are being discussed.

Imagine you wish to analyse many online reviews concerning your product or services. Keyword extraction helps you to shift through the complete set of information and obtain the words that best describe each review in just seconds. That way, you will be able to simply see what your customers are mentioning most often, and it will save your teams many hours of manual processing. Key phrase extraction works best when you process large number of documents or datasets of text, this can be opposite from sentiment analysis, which performs better on smaller amounts of text. This capability is useful if you wish to quickly identify the most important key word or key phrases in a collection of documents, for example given input text " Communication was great and delivered the work right on time.” the service returns the most relevant key phrases like: "Communication was great " and "right on time ".

Phase three: Document classification.

Here I would discuss most important factor clustering of documents in the system to increase the performance of the automated duplicate identification process using Machine Learning (ML) in the form of Natural Language Processing (NLP). Document classification having two different methods: manual and automatic classification. In manual document classification, we use to understood the meaning of text, identify the relationships between concepts and categorize documents. After the manual review of 1000 of documents, automatic document classification applies using machine learning or other technologies to automatically classify documents this results in faster, scalable and more objective classification. Document classification involves creating a dataset, pre-processing and the most important a right strategy. First of all, in the dataset we are required to put importance and weighting to each and every keyword/phrase when creating document vectors. We could do some pre-processing and decide to give different weighting to keyword/phrase based on their importance while making the strategy. These strategies will help us to classify the document by comparing the number of matching terms in the document vectors.

Phase four: Duplicate detection.

Duplicates detection can be done using a combination of matching rules. When a matching rule is activated, single or multiple match rules is applied for near duplicate documents detection, and the best match result is returned. The matching rules will grow with documents processed and get matured with AI using semantic similarity measures, to apply semantic analysis to the similarity assessment of a pair of documents. The indexing process improves performance and returns a better set of match results.

Hopefully this article provided a little more insight on near duplicate document detection and the best way of Document classification. – Happy Programming!