Document Processing Machine Learning in KlearStack AI

Ashutosh Saitwal
Ashutosh Saitwal

Founder CEO - KlearStack AI

Table of Contents

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[vc_row pix_particles_check=””][vc_column][vc_column_text]Businesses leverage intelligent data capture and optical character recognition (OCR) types of solutions to automate document processes and improve the accuracy of data extraction from documents. These solutions are useful to a certain extent.

A major drawback with traditional OCR-based systems is that it extracts data based on the surface level of information. Documents such as invoices, receipts, purchase orders and other similar document types require a contextual understanding of the documents. This is where the role of document processing machine learning becomes crucial for enterprises. Solutions that leverage artificial intelligence and machine learning capabilities can accurately extract data from unstructured documents.

But before we understand the role of artificial intelligence and machine learning in document extraction solutions, let us understand what is artificial intelligence and machine learning.

What is Artificial Intelligence?

Artificial Intelligence is quite a broad term. In the context of document automation and data extraction, it helps to automate mundane and repetitive tasks. In other words, it is used for the optimization of existing processes. Data entry work from documents like invoices, receipts and other related documents becomes quite monotonous. By leveraging artificial intelligence, data entry work can be automated even for documents that are not organized or structured properly.

What is Machine Learning?

Machine Learning is a subset of artificial intelligence that can train platforms to learn from datasets input via documents and enhance the process, over some time. Just like how a human evolves with new knowledge that they have gained, machine learning aims to do the same by mimicking human capabilities and evolving the system.

An algorithm is a sequence of statistical processing steps done in chronological order in the field of data science. In the context of machine learning, algorithms are trained to find patterns based on new data that has been inputted. The more efficient the algorithm, the more accurate decisions and predictions can become as more and more data is processed.

The more data is gathered, the more variables the platform will have that will enable better decisions to take place. As computing becomes more affordable and powerful in near future, data scientists can build more precise decision-making platforms and make accurate predictions since more data is processed.[/vc_column_text][vc_column_text]

How Document Processing Machine Learning Works in KlearStack AI?

[/vc_column_text][vc_single_image image=”8604″ img_size=”large”][vc_column_text]Broadly, there are five main stages when it comes to accurate data extraction and document automation for solutions that are powered by artificial intelligence. Each of the stages has been explained below in detail.

  1. Importing Data
  2. Classification, Tagging & Indexing of Data
  3. Optical Character Recognition
  4. Accurate Interpretations of Symbols
  5. Decision-Making by Enterprise

 1. Importing Data

The initial step is to enter data into the platform. Setup the processing of document by the format of documents that needs to be scanned. Documents could be scanned images, hard copies, PDFs, spreadsheets etc. KlearStack AI can support documents of various types and ensure that data is accurately extracted from them. Documents from data cannot be extracted accurately, it will alert you for potential errors and notify you of them.

2. Classification, Tagging & Indexing of Data

Once the data has been imported, the data from documents that are to be extracted has to be categorized, tagged and indexed. For example, let us assume that an invoice has been scanned. Details such as vendor’s name, line items, unit amount, total amount, tax, and discounts (if any), have to be labelled and put into proper columns.  Once the data is categorized, tagged and indexed, it becomes easy for the solution to understand and interpret the data in its contextual sense.

3. Optical Character Recognition (OCR)

The third step is the transformation of the data. This is achieved using OCR. OCR helps to convert text into machine-readable documents that can be easily edited by humans in case there are errors in the documents. This step is crucial as it helps to make the text in documents easy to edit and resave files in case of any discrepancies in the document.

4. Accurate Interpretations of Symbols

Punctuation symbols such as dots, commas, semi-colons etc are tricky to interpret and extract at times. Putting it into context is another hassle altogether. With KlearStack AI, however, it is a hassle-free process as it ensures that all the text and symbols are contextually understood by the platform before they are interpreted and extracted.

The essential role of artificial intelligence is to update the systems as to what it is exactly looking for. Whether this document is an invoice, receipt or purchase order. Enterprises can create templates for each, and search for a match. However, this process is again time-consuming and a highly unreliable method of getting things done. It is better to let the system learn and evolve on its own and consider the context. This method is much better and has proven to be more effective.

5. Decision-Making by Enterprise

The final step is to make informed decisions based on the data that has been gathered. Document processing machine learning has automated the entire data extraction process. Now it’s time for the management to ensure that decisions that are taken are based on data and the best interest of the enterprise.

Summing Up

Document processing machine learning is an important part of intelligent document processing. KlearStack AI is well-equipped to streamline and automate your document process, end-to-end. If you wish to know more about our solution, schedule a demo with our experts. [/vc_column_text][/vc_column][/vc_row]

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