Why OCR Fails? 5 Limitations Of Conventional OCR Engines

Why OCR Fails? 5 Limitations Of Conventional OCR Engines

by Amit Jnagal, on June 12, 2019 11:00:00 AM PDT

While conventional OCR engine is considered as a mainstay platform and might seem like the end-all, be-all solution for capturing data. It can be frustrating at times when the data is misread or not even be read. Inputting a document into an OCR doesn’t mean that it will actually output something without a hitch- forcing knowledge workers to manually work.

Why does OCR fail?

Many OCR engines fail to support and understand the complexity of the input data in a given document. For example, if the input document is a form then the OCR might identify the text but may not recognize text over a line of the text in blocks. This may result in unexpected output.
Conventional OCR tools don’t possess the quality to process documents of different formats. For example, an OCR for accounts payable can be well accustomed to read printed text only and fails to read handwritten documents.
Certain OCR platforms only identify the font of the first alphabet in the line and continue to read considering the initial font and fail to understand different font sizes in the same line.
Many OCR solutions fail to read tables let be bordered or borderless thereby leading to a higher risk of unexpected errors.
Conventional OCR engines fail to remove noises such as black spaces or garbage values which leads to uncertainties in output.

With all these flaws in place, there is a high risk of compromising OCR accuracy and quality of output. As a consequence, the conventional OCR platform is unpredictable and the lack of accuracy presents a major challenge making decision-makers think what are the factors that affect the accuracy of OCR? And how to address these issues.

Let’s explore how can multiple OCR frameworks be advantageous over regular OCR.

In contrast with conventional OCR, multiple OCR is a more powerful tool that has the capability to hold the salutation of being a one-stop solution to all the regular OCR challenges. 

Where many OCR engines fail to achieve modification or conversion of any form of text or text-containing documents such as handwritten text, printed or scanned text into an editable digital format for deeper and further processing. Multiple OCR solutions can intake all the formats and types effectively and efficiently. Multiple OCR engines can completely ingest, process and execute resources of any format, type, and source, let be a text or table. The document understanding of multiple OCR solutions is quite effective which decreases the chances of errors thereby intensifying the accuracy of the output. 

By making it possible for teams to work better and smarter, multiple OCR helps them to optimize their work process, enabling them to analyze a broader and deeper set of data for future insights.

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Topics:Intelligent AutomationAI ReadinessBusiness Insights

About this blog

AI can be a game-changer, but only if you know how to play the game. This blog is a practical guide to turning AI into real business value. Learn how to:

  • Make sense of complex documents and images.
  • Extract the data you need to drive intelligent process automation.
  • Apply AI to gain insights and knowledge from your business documents.

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