Data Entry

How AI and Deep Learning have Revolutionized Document Processing Automation!

Pankaj Tripathi
Pankaj Tripathi
Jan 30, 2023
 min read
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How AI and Deep Learning have Revolutionized Document Processing Automation!

The Covid 19 pandemic has been the biggest driver for digitization.  To provide services, machines need to understand you data stuck inside of documents.  
The biggest challenge lies in digitizing data and speeding up the process as the majority of our tasks still happen on paper or PDF files. From getting signature on a report card to filing for a loan there is a huge process of documentation which is highly time-consuming and requires a lot of manual processes.
Companies spend a huge amount of money and resources in managing these documents and keeping track of them. In all the highly regularized sectors like healthcare, banking, legal, supply chain, etc, it becomes even more critical especially during audits. So automating this process becomes of utmost importance.

What is document processing automation?

Document process automation is the design of systems and workflows that assist in the creation of electronic documents. These include logic-based systems that use segments of pre-existing text and/or data to assemble a new document. This process is increasingly used within certain industries to assemble legal documents, contracts, and letters. Automation systems allow companies to minimize data entry, reduce the time spent proof-reading, and reduce the risks associated with human error.

How are AI and Deep learning winning this game?

The document process automation is the need for the digital era. A lot of work was happening in this field but recent developments using cutting edge technologies like deep learning and Artificial Intelligence have completely revolutionized this domain.

The earlier approaches were more focused towards extracting features from images using different techniques like edge detection, Gaussian filters, etc which had many limitations in real-world use cases. However, with the enhancement of deep learning models, you do not have to explicitly extract features from the image using any pre-processing techniques, rather you need to train your model using input and output images and your model automatically learns features from those images.

AI Document processing wrkflow

For example: The above algorithm represents the most advanced model that uses Optical Character Recognition (OCR) service to extract the text and layout information, which allows you to work with native digital documents, such as PDFs, and document images (e.g., scanned documents).

How is AI automating the documentation process workflow?

AI Documentation processing workflow

Document process automation workflow comprises of following steps:-

1. Data Ingestion

The data source is the primary channel of extracting information (data), whether the data is structured, non-structured, or is in any other format. Data Ingestion is the process of reading data through various channels including PDF, Excel, Mails, Word, Scan file etc.

2. Data pre-processing

This step requires image and data pre-processing steps like cropping, noise reduction, and filtering which eases the data extraction process.

3. Data capture

One of the most critical steps of whole workflow is extracting relevant information. OCR is one of the most advanced technologies and is backed up by different machine learning algorithms. Different computer vision models and libraries like CNN and OpenCV are available which help in detecting and extracting text.

4. Data classification/Indexing

After extracting information and text from the source, classification, or indexing that information according to the template is a major challenge. For instance: while extracting text from invoices, it is vital to differentiate Date, Amount, Name, and other fields from the text you have extracted. Here, Deep learning models come to the rescue that label the data according to its category and automate the whole process.

5. Data extraction

Now, the information that you extract from the above process could be in different formats and also could be text or an image. Techniques like NLP and computer vision contribute to understanding the underlying data.

6. Data validation

The most important step is the verification of data and the quality check. This step can be automated using a template-based approach.

Document processing automation use cases

1. Finance

Extracting data from bank statements for reconciling records and comparing them against the company’s own records was manually done via complex spreadsheets.

2. Insurance

Claims processing is at the heart of every insurance company. Since customers make claims at a time of misfortune for them, customer experience and speed are critical in claims processing. There are numerous factors that create issues during claims processing such as

  • Manual/inconsistent processing: Claims processing often involves manual analyses completed by outsourced personnel.
  • Input data of varying formats: Customers send in data with various formats
  • Changing regulation: No insurance company has the luxury of not accommodating to changes in regulation in a timely manner. This requires constant staff training and process update.

3. Logistics

Trade finance involves multiple parties coordinating and ensuring the delivery of goods and payments. Banks and companies communicate through letters of credit and other documents that need to be processed.

The processes that have been talked about above can easily be automated.

We at Docsumo, can do that for you and can save you the trouble of doing everything manually. We not make automation possible, but also ensure accuracy and adaptability. While we work on making our product even better, get in touch with us and sign up for a free trial now!

Suggested Case Study
Automating Portfolio Management for Westland Real Estate Group
The portfolio includes 14,000 units across all divisions across Los Angeles County, Orange County, and Inland Empire.
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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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