The huge number of documents that produced during the course of business operations are humongous. From invoices and work orders to manuals and knowledge assets, there are many documents captured and shared every hour within and outside the organization.

Managing these documents, classifying them, and converting physical papers to digital ones require a tremendous amount of manpower. Plus, the human effort involved made the document classification process error-prone. This cost puts a strain on the company's bottom lines.

AI-based Document Classification 

Fortunately, a smarter way of managing documents is now available with AI-based document processing. AI-based document classification helps an organization streamline its document management process. This way, these assets remain secure, shareable, and scalable. The process is fueled by Natural Language Processing (NLP) and Machine Learning (ML). Their algorithms keep tagging the documents based on various factors in the document they will be scanning. The more documents they scan, the better they get at learning about the organization-wide policy on document production, management, and classification

An intelligent classification model overcomes issues like errors and a lack of scalability. The best part is that it accelerates efficiency within the organization. In turn, this USP leads to informed decision-making in a shorter span of time compared to the manual classification process.   

Use Cases for ML led document classification 

The smart use of AI technology like NLP and ML can help alleviate the risks of the nightmare that we know as manual document classification. These technologies can help identify and classify the type of document and extract key date to assist in the insight generation process. They can read structured as well as unstructured data and then assign a category accurately.  

Here are some cases where AI-automated document classification can help businesses –

1 – Automatic routing – If a single point of entry receives thousands of documents every day, it needs to know whom to send it to. This goal is something that can be achieved by a document classification algorithm easily. Suppose the system gets a lot of support tickets. The ones marked as 'bills' will be sent to the IT, others marked as 'bugs' will be sent to the IT team.
2 – Sentiment analysis – You can also gauge the overall sentiment behind the document the system receives. So if it gets documents that carry negative sentiment, then it will be marked for urgent responding and action to avoid escalation.
3 – Identification - AI-based classification can easily identify genre or language in a document. This way, you can segregate the documents based on whom it has to be sent to in a country's offices.   

How does AI document classification expertise add value to a business?

Take the example of a recent document classification project executed by CIGNEX Datamatics.

It worked with a Global Information Service Provider and Publishing Company with more than 15,000 employees across 150 countries worldwide. Their manual efforts were eating hours of productivity for multiple people across different offices. They mandated the tech company to come up with an intelligent solution that can be deployed quickly and scalable as per the company's needs.

The solution receives inputs in varied forms like XML files and PDF documents. They also get the content detection and analysis framework that conforms to the organization's business practices. They are passed through a training-based classifier, DL4J, Naïve Baiyes, and TensorFlow. They evaluated machine learning and neural network tool. They also helped create a model and run a test set. There was a custom reviewer to deal with anomalies and outliers. They also served to enrich the training set for better quality machine learning.

As a result of the solution, the client achieved a high degree of accuracy. Additionally, it was built to be scalable to the future demands of company business.

Here is a snapshot of the capabilities we have developed over time with our work on NLP based document classification system

  • Dataset building, cleaning, OCR to Text, algorithm selection for accuracy, insights mapping to domain remain among the top concerns
  • In the supervised part, we have been able to achieve good accuracy when we worked with standard libraries in Python.
  • Unsupervised learning brought a different set of problems and advantages. It brings automated category discovery as an advantage, but also creates a problem of tagging the names for categories. Google solved such problems of image classification by asking users to label images using captcha for the check of human vs. robots
  • Additional insights as a layer on top of it that we could generate for our customers were:

    • Metadata insights around authors, versions, the average age of documents, editors, etc.
    • Content insights included top 20 & bottom 20 words, top 20 influential sentences, relationships of categories, documents with words, weighted list of words, sentences, influencers to a category, category mapping improvement suggestions and so on, complex cleaning of intersecting non-unique categories
    • De-duplicated and unimportant word removal
  • Document insights feed into multiple functional domains for intelligence like:
  • In Finance – we can get document categorization, contract insights, budget insights, audit/compliance help, fraud detection, mapping of Account payable to account receivable and more
  • All such insights which are mapped to domains like Finance, Operations, HR, Marketing, Publishing, Education, Chemicals, etc. have some sort of base in document content & meta-data insights at the technical level. Multiple company level trends like engaged workers, frequent editors, plagiarism check, authoring frequency, etc. can be built out of document classification & natural language processing
  • Technologies used:

    • Python 3.x with Anaconda Navigator & Jupyter
    • Machine Learning libraries & algorithms
    • NLTK
    • RPA (UiPath) to convert PDFs to text
    • Open Datasets from websites like Kaggle & Google Dataset search

Get the power of AI-based document classification capabilities in your company

Want to gain better efficiency and unlock insight generation from document management? Then connect with us at CIGNEX Datamatics. We will help you gain maximum ROI from your investment into a future-ready AI-based document classification system.

The easy to use interface and customization options provided by our solution help you quickly adapt this system to your organizational resources.