Why AI Is the Next Step in Document Processing

Document processing is one of the most time- and resource-consuming tasks, compared to the value added that comes from the output.

It takes hours of manual, diligent work to input data in invoices, update medical records, or create the folders for insurance claims.

If all of these tasks could be done automatically, we’d see important benefits, including better organization of the underlying data, automatic scanning of records, error-checking, and pattern detection.

Some of the most common applications of such automation include finance and accounting management, electronic health record updates, healthcare insurance claiming, personal and national security, logistics document management, and clause detection in legal documents.

Document Processing Steps

Automated document process isn’t much different from the old-fashioned, human-driven way of doing it, but it offers guaranteed accuracy, can run 24/7 without getting sick or being late, and never gets bored.

Although more intermediary steps can be added, there are five main stages of AI-powered document processing:

  • Importing data into the system;
  • Data classification, tagging, indexing;
  • Optical character recognition
  • Correct interpretation of symbols, in context
  • Decision-making.

1. Importing data

The first step is getting data into the system and processing it depending on its original format. Some documents exist as hard copies, some as scanned pictures, while others are already in a semi-tabular format, such as fill-in pdfs or spreadsheets. A truly helpful processor would support multiple formats and could extract information from each of them accurately, or at least signal possible errors due to input quality.

2. Data classification, tagging, indexing

Next, the extracted information needs to be classified, tagged, and split into categories and components. For example, when reading an invoice, the machine needs to identify all the different items like the vendor’s data, the buyer’s data, amounts, taxes, discounts, purchased products, and internal codes like the contract number.

Once the areas containing such data are delineated from the rest and the document quality is checked, the algorithm can move forward.

3. Performing optical character recognition

The third thing that needs to be done is the actual OCR (optical character recognition) of the input data. The goal at this point is to convert everything into a format that can be read by machines and easily edited by humans in case of errors.

4. Correct interpretation of symbols, in context

The real challenge here is to interpret symbols correctly and put everything in context. For example, dots and commas are both used to designate deciles and thousand-fold numerals.

It’s crucial to acknowledge which convention is used in each of the processed documents; otherwise, there’s a risk of substantial computational mistakes.

The first role of AI is to let the system know what it’s looking at. For example, is a given document a receipt or is it an invoice?

A way to solve this is to create templates and look for matches, but this is a time-consuming and unreliable technique, since every merchant has their own forms and documents. A better way is to teach the system to look at the context — the method successfully applied with natural language processing.

5. Decision-making

Last but not least, it’s vital to use the acquired information to make intelligent decisions. This is the step that differentiates traditional OCR and the kinds powered by AI.

Until now, all data retrieved from documents was analyzed by humans who generated reports and made decisions.

The leap forward exists in the possibility of letting the algorithm take on some of the work, including sending reminders, making automated payments when specific criteria are met, and replying to customers through chatbots.

Use Case for Smart OCR

Logging in transport documents, such as tickets, to get a reimbursement is time-consuming, tedious task. Just imagine the hassle a commuter would have to undergo to get a refund from their employer for their daily travel to work.

Additional hassle comes from the need to ensure better management of ticket processing, including sales, processing delay claims, and checking availability due to cancelations.

Since most travelers are reluctant to adopt electronic ticketing, the responsible staff may need to process paper tickets. This is a problem solved by smart OCR and a traveler’s smartphone.

Such a system would consist of different modules performing the five steps mentioned above.

The imaging module of the app captures the ticket. Next, the processing module applies light, contrast, and perspective corrections if necessary to make the information on the ticket readable.

An OCR tool scans the ticket, classifies it, and outputs the relevant information such as the time of travel, ticketing class, distance, and more. Combined with external data such as the current date, time, and location, the neural network analyzes the dataset and outputs the results, like a reimbursement approval or the issuance of a new ticket.

Safety Issues and Final Thoughts

As with every technological advancement handling personal data, there’s the accompanying problem of privacy and security. These concerns affect all the steps of the process, from the human verification of accuracy to the results of automatic decisions. However, there are solutions to ensure data anonymity.

For example, in the case of passport scanning, the algorithm blurs the picture and splits the data into separate fields on the client side.

The information gets sent to the servers in an anonymous form: it’s impossible to determine which field belongs to a particular person. The fields are recognized separately and are sent back to the client using HTTPS encryption.

Even if this technology is not yet in every office, we can expect it to become quite popular due to the critical time and cost savings it can generate. It’s all about working smarter, streamlining processes, and turning heaps of paper into actionable and well-indexed information.

When choosing a smart OCR solution, be sure to ask the provider about ways to integrate it seamlessly into your current business processes and technology, as most employees are reluctant to changes.

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Our team has been at the forefront of Artificial Intelligence and Machine Learning research for more than 15 years and we're using our collective intelligence to help others learn, understand and grow using these new technologies in ethical and sustainable ways.

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