Optical Character Recognition

A Quick Introduction to Automated OCR Data Capture

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A Quick Introduction to Automated OCR Data Capture

Optical Character Recognition (OCR) is an automated process that converts text-based images into computer-ready text that you can then edit and manipulate. It’s a faster way of capturing data that works by scanning documents and converting them into text, and pushing extracting data directly into a database or third-party software.

What is OCR? How is it better than manual data entry? Are there any better alternatives to this technology? -  We’ll answer all the questions in this article. 

So, let’s jump right into it:-

Processing documents manually is slow, tedious, and extremely expensive - businesses must continually develop innovative ways to combat costs and streamline operational processes. Great deal of text-based information is locked in physical formats that are not easily accessed for analytics. That is until you unleash the power of Optical Character Recognition (OCR). OCR is revolutionizing how companies collect data from physical documents and making the data accessible.

A Comparison of Manual Data Entry and Optical Character Recognition

Traditional data capture is slow, tedious, and expensive. It's also prone to human error. Companies that OCR technologies primarily serve well are process payroll and admin tasks. They can reduce the number of forms they have to fill out and speed up their submission process.

Manual Data Entry and its challenges

Manual data entry is the process of entering information from a paper document or image file into a computer application. The manual way of sorting through and data entry can quickly become a bottleneck for companies that are trying to scale their operations.

There are many challenges associated with manual data entry including:=

  • The challenge of reading certain texts due to poor handwriting or printing quality
  • The Difficulty identifying important documents versus those that aren't so important
  • The time it takes to manually enter all the necessary data into your computer's database
  • The potential for human error when entering all this information by hand

On the other hand, automated OCR data capture can identify, extract, and classify useful information from documents. Amongst other advantages of OCR over manual data entry, the most significant benefit is speed. OCR is fast, accurate, and more reliable to handle your data entry needs.

How OCR is better than manual data entry?

Automated OCR software is more convenient as it does not require any intervention on the user's part for its operation. Compared with manual data entry, automatic OCR systems are more economical because of the same reason.

Automated OCR data entry can instantly turn your product information into computer-readable text that shopping carts and other online systems can use. OCR software reads the text directly from images of items and then converts it into usable information, allowing you to quickly and easily upload it.

Drawbacks of automated OCR data capture

OCR is far from perfect, and even the most basic text recognition software isn't infallible. For instance:

  • Most fonts are not supported by OCR software (and when they are supported, there are often significant issues). This means that text from any document with a font size of fewer than 12 points can likely have errors in it.
  • Text from documents that use fonts with bold or italic type can be more challenging to convert. Also, paragraphs and line breaks may not be recognized by OCR programs and must therefore be manually added to the file.
  • OCR software fails to correctly pick up certain characters in your document; this may happen if they are extremely large or small or not in a standard format.
  • You must double-check all spellings of words and punctuation before submitting your documents.
  • The text capture does not include images or tables, so these can be scanned separately.
  • The finished scan is a single-column page, meaning that there are limitations on presenting this information online or in other formats.
  • OCR doesn't recognize all formatting so that words may be written in all capitals or some lines may be indented.
  • If your documents are large in size (more than 5,000 characters) or have very complex formatting, it might not be cost-effective for you to use OCR. 

Is IDP a better alternative to OCR?

While OCR may be adequate for an occasional task, IDP offers the ability to manage high volumes of complex documents with greater efficiency and accuracy.

Here are the reasons why IDP is a better alternative than OCR:

  • Using IDP means you can extract structured data from unstructured documents with higher levels of accuracy than OCR alone.
  • It's also very adaptable because it allows for the extraction of images, tables, and other types of data present in many different types of documents.
  • IDP uses intelligent algorithms to extract the text from scanned images.
  • It captures relevant information without errors and by avoiding missing any key data
  • The high degree of automation involved enables faster processing times.
  • IDP can analyze multiple document types without any pre-training or manual rule creation.
  • IDP can be deployed across your entire business, regardless of your industry or market sector. You can use it for extracting data from customer service emails or purchase orders from suppliers in real-time.

When it comes to accuracy, OCR systems don't come close to what IDPs can do.

Here is an overview of the comparison between OCR and IDP:-

Optical Character Recognition Intelligent Document Processing
Type of Documents Simple, easy-to-use templates with a well-defined structure Complex, unstructured documents with no particular form or template
Level of Accuracy Low High and extremely precise
Type of Algorithm Manual process with the minor intervention of a tool Machine-learning and AI
Adaptability Within the purview of data extraction only Intuitive and seamless interface
Multiple Output Formats JSON, Excel, CSV JSON, Excel, CSV
Scalability Low level of scalability High level of scalability with plugins and customizable features available
Analysis Basic and preliminary level of analysis Deep insights into processed data to generate better outcomes

How to automate data capture with Docsumo?

Automated data capture is a core strategy for improving your conversion rates. If you're interested in automating your form processing, you'll want to take a look at Docsumo's software. It's a powerful tool that can help you leverage OCR (Optical Character Recognition) and Machine Learning(ML) technologies to extract and process data from forms automatically.

Automated form processing can save you hours of manual data entry, lower your costs, and more.

1. Upload Documents

You can upload documents that contain information about your customer profiles, like customer names and addresses.

2. Edit Fields

To ensure accuracy in your data entry, remember to double-check all fields for accuracy. For example, if the parser classifies a data field incorrectly, make sure to fix it.

3. Validate Fields

If there are any errors in the information you are capturing on the form, be sure to fix them before moving on to the next step.

4. Review and Approval

Once you have captured every piece of information on your form, it's time to review and approve it before moving on to the next step. This ensures that every last part of the information is accurate and there are no errors before uploading it.

5. Download CSV/Excel/JSON file formats

Docsumo lets you download the data in CSV/Excel/JSON formats for post-processing and analytics.

Wrapping Up

Automated data capture is beginning to gain traction and is helping businesses around the world deal more effectively with their ever-increasing workloads. With so many data capture solutions available in the market, it can be overwhelming to choose the right one for your business. We hope that this resource provides you with a better understanding of OCR so that you can make an informed decision about the technology for your next implementation.

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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