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In this blog, we will discuss how OCR works in form processing, its benefits, and how it can improve the efficiency of your existing workflow.
For most high-performing businesses, efficiency is key. And with increasing transactions, processing forms and documents manually has become a major challenge.
It’s time we find a better alternative and adopt smarter OCR technologies to automate form processing. Optical Character Recognition (OCR) technology has revolutionized how businesses process forms by automating data extraction with speed and precision.
From reading printed or handwritten text to converting it into editable digital formats, OCR form processing offers a reliable solution to streamline operations and reduce manual workloads.
This guide outlines a practical, step-by-step approach to implementing OCR for form processing in your organization. Whether you’re exploring ways to enhance your existing systems or considering OCR for the first time, this resource will help you navigate the process with clarity and confidence.
Let’s jump right into it right away by understanding different types of forms.
Lenders, insurers, and other industries need to process numerous forms in their day-to-day operations. These forms can be divided into two categories:-
i) Structured Forms
ii) Semi-structured Forms
The division is made on the basis of structure, template, and layout of different forms. This classification is important as it affects how these forms are processed.
Let’s have a look at both types of forms one by one:-
Structured forms are made up of clearly defined text blocks with fields that are always in the same place. They only change in terms of the information populated in each field. OCR works well with structured forms because the data remains in the same place on each page.
Examples: Registration cards, Surveys, DMV forms, etc.
This fixed structure of forms allows for higher data extraction accuracy. However, there may be other factors that can affect the OCR accuracy negatively when information is typed over the lines of the documents.
For example, if “1” is typed over a field and the lines get too close, the OCR engine may not capture the number “1” at all.
For semi-structured forms, the location of key identifiers and checkboxes vary along with the data fields. This poses a problem for template-based OCR software as it may capture incorrect data, which might be located somewhere else on the page.
Data extraction from semi-structured forms relies upon the use of business rules to locate the 'position information' for a data point. These rules rely upon the fact that the data to be extracted is always in the same relative position to a defining characteristic.
Example: Invoices, Bank statements, etc.
For obvious reasons, set-up procedures for unstructured forms can work for fixed-structured forms, but not vice versa, as fixed-structured forms require strict data placement.
Again, certain external factors might make an originally fixed-structured form more suitable for the unstructured category.
Say a PDF was sent to multiple clients to be printed, filled out, and returned to you - a lot can go wrong. Some users might scale the PDF, printing in different sizes; others may use different printing margins or have varying color intensity, and again, there are going to be glaring differences between the forms that are faxed, scanned, and sent in original.
All these external factors add to the woes of agents responsible for entering data manually or using traditional OCR to scan these documents. But there is a way it can all be automated.
Now that we understand the key differences between structured and semi-structured forms, let's explore some real-world examples of how form processing can be applied across various industries.
OCR finds its application in almost every industry, but certain sectors are more data-intensive. Let us look at some of them.
Mortgage lending has strict guidelines for paperwork that must be met to satisfy both mortgage insurers and investors. However given the lack of standardization, processing these documents is mostly a manual operation.
Several of the forms — Form 1003 (industry standard Mortgage Application), Form 710 Fannie Mae (application for mortgage assistance due to financial hardship), and Form 1008 (Uniform Underwriting and Transmittal Summary) are extremely crucial documents for assessing risks related to mortgage lending. And processing these forms manually means a delayed time frame adds to the possibility of human errors.
Data extraction solutions, like Docsumo, help expedite these processes by extracting and validating data in real time. This ensures that the documents provided by the borrower are secure and that the data extracted from these documents is actionable, thus automating the mortgage lending process.
Manual data entry is a costly and time-consuming affair, more so during the tax season when thousands of tax documents must be processed in a given time frame.
Docsumo’s intelligent document processing capabilities expedite tax form processing and help you put your man-hours to better use.
Here are the tax return forms Docsumo can process instantly:
Using Docsumo, you can automate the processing of all the above documents in no time and with minimal setup — all the while maintaining accuracy and improving workflow speed.
Handling monthly payslips in an optimized manner is a challenge HR personnel in almost every industry sector face. This is mostly because the payslips are processed manually. On top of being incredibly tedious, manually handling these salary slips adds to the operational costs of a company. And here’s where Docsumo comes into the picture.
With its payslip automation API, all fields from payslips, including employer and employee names and addresses, salary period, days/hours worked, gross salary, tax deductions, etc., can be seamlessly extracted with more than 98% accuracy and within 30 seconds.
The present infrastructure for processing medical and healthcare forms manually is inefficient. Data takes hours to be fed into the system in an industry where time is of the essence. This directly adds to your patients’ misery, thereby hurting your bottom line. While traditional OCR has been considered as an alternative solution for text mining, machine translation, and text to speech — it struggles to perform as per expectations in the healthcare sector.
The medical industry is riddled with duplicative and redundant manual processes which are dependent on organizational data silos. And with everything being data-intensive, achieving digital automation is of the utmost importance.
With the help of automation tools, the processing of the following forms can be expedited significantly:
DS-3057: Medical Clearance Update
Insurance, like other industries listed above, is paperwork-intensive. Dealing with forms is a part of the daily routine for an insurer. And out of all the forms, we’ve all heard of the ACORD forms.
ACORD, or Association for Operations Research and Development, is an international non-profit organization that aims to standardize insurance forms and eliminate all the noise and clutter. Nonetheless, copying and entering data from these forms isn’t an enjoyable process, hence the need for a smart OCR.
ACORD forms are available in all formats, including eForms, PDFs, and electronic fillables.
Here are some of them:
With Docsumo, Acord forms can be processed in real-time with over 98% accuracy, thereby saving insurance agencies a ton of time, effort, and capital.To understand how these use cases are achieved, let's now explore the underlying technology: OCR form processing working.
Let’s take a detailed look into the steps involved in OCR form processing:-
As the first step of OCR form processing, the format of the file is identified. It is done to change other formats into images which is essential to perform OCR.
In this step, the quality of the scanned image is improved with noise reduction. Noise is a random variation of brightness or color in an image that makes it difficult to identify the text from the background. Blurring or Smoothing of the image is also performed at this step removing “outlier” pixels that may be noise in the image.
Structured or semi-structured tables both include key-value pairs and tables in some form.
In this section, we discuss how OCR is used to extract line-item data and key-value pairs:-
OCR is a fundamental data extraction technology but nowhere close to being perfect.
Let’s have a look at some of its limitations when it comes to form processing:-
Understanding these limitations is crucial for implementing a successful form processing automation strategy. Let's explore how you can effectively automate form processing with IDP.
Intelligent Document Processing (IDP) is a better alternative to OCR as it helps overcome the limitations of OCR.
Benefits of Machine Learning and Artificial Intelligence-based form processing include:-
1. Scalability - As a business, you can process more forms as compared to manual form processing. IDP solutions can adapt to any layout/template changes, so you don’t need to retrain the solution for the most recent form version.
2. Growth - Extract data from forms automatically and help people concentrate on more important tasks. Grow your team, as you don’t need to hire people for manual data entry.
3. Accuracy - 99%+ field level accuracy for form processing, which is not possible manually. Docsumo’s document AI solution offers over 95% Straight Through Processing which means you don’t even have to look at 95% of the total forms you process, and they get processed automatically.
4. Analytics - With Docsumo's automated form processing APIs, you get better data quality using document-level data validation. Data validation against your database adds to this accuracy.
If you’re looking to automate form processing and digitize business workflows to offer better services to your customers, let’s see how Docsumo can help.
Forms are essential in almost every industry for simplifying daily operations. However, since the data in these forms needs to be digitized for further processing, we need a more permanent solution than manual data entry.
Here’s how Docsumo’s form-processing software facilitates automation:
After signing up on the Docsumo platform, upload the forms on the portal in either image or PDF format. You can choose to drag and drop the documents either directly from your email or the local system.
Through a combination of reverse image search and neural networks, the entries in the forms are extracted using OCR. The extracted data can be edited manually if required.
Docsumo leverages NLP, Computer Vision, and advanced Deep Learning to assign each extracted bit of information the right data type. Not only does this help improve the accuracy of value extraction, but it makes the data ready for consumption directly by third-party APIs or software.
After the entire data extraction and validation process, Docsumo prompts you with a few optional key-value pairs. You can choose to either ignore or accept the prompted suggestions. But as soon as you approve the suggestions, the file is saved.
Now, your file is ready to be downloaded in CSV, Excel, or JSON format. While CSV works well for contact information and databases, you can either choose Excel for analytics or JSON to send the data to other software. The system can process multiple forms or documents simultaneously — simply select the data you want to capture and leave the rest to Docsumo.
If you’re looking to automate form processing and digitize business workflows to offer better services to your customers, schedule a free demo with Docsumo, now.