OCR (Optical Character Recognition) certainly plays its part in enabling business processes in a company to run with less manual effort. But as we’ve seen, traditional OCR doesn’t stand a chance against machine learning (ML) based OCR when it comes to more complex automated document processing. ML-based OCR takes on all forms and formats, structured, semi-structured, or completely unstructured. But what about handwritten text in documents? Here, a whole new spectrum of complexity is added. What is clear is that Handwritten Text Recognition (HTR) is hugely important in automated document extraction, and for successful business transformation, the digitization of handwritten text is an essential building block. It is only with powerful Artificial Intelligence that accurate and therefore effective data extraction from handwritten text is also becoming increasingly possible, bringing great benefits and significant advantages to businesses.

Handwritten Text Recognition (HTR) as the next level of intelligent OCR

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The need for HTR is great

Even with digitization and the majority of documents in electronic form, handwritten forms are still part of daily business, more so in some industries and less so in others. We are talking about little handwritten text, such as individual notes and comments on documents, up to completely handwritten forms. In all cases, whether individual annotations or entire documents, handwritten text leads to their digitization via manual processes. This not only severely slows down all other business processes, but is also very labor and cost-intensive and represents pain points for companies that want to be solved as quickly as possible.

Convert unstructured and handwritten texts into structured formats automatically to guarantee the smoothest possible business processes

Several industries particularly affected

The need for Handwritten Text Recognition for companies is therefore great. Here’s how several industries are particularly affected and grapple with handwritten documents on a daily basis:

  • Healthcare: In healthcare, in many places, medical prescriptions are still filled out entirely by hand by the doctor, making their processing tedious and slow
  • Insurance: In insurance, claims are often filled out by hand, and claims documents must then be processed manually, which is unnecessarily time-consuming
  • Banking: Depending on location and country, hand-filled checks are still the daily bread of bank employees, which significantly lengthens business processes and is anything but a plus for customer satisfaction
  • Libraries: When it comes to the importance of digitizing handwritten texts, the millions and millions of documents and millions and millions of pieces of data from historical writings and valuable information in libraries for their preservation should not be forgotten either

Powerful AI for handwritten text recognition and extraction – the Parashift Platform enables just that

OCR can’t cope, only HTR makes it possible

So the thing is, OCR can’t tame the beast of handwritten text recognition. And by beast, I mean major challenges of handwritten text for capture and extraction, including the following:

  • Moderate to poor quality of the document (clutter effects)
  • Huge variability in handwriting, different slants, inconsistencies, and so on and so forth

Conventional technologies are not sufficient for HTR. It needs powerful AI built on deep learning-based approaches that can provide data with higher accuracy. This is exactly what Parashift’s powerful Artificial Intelligence delivers, which is groundbreaking for the accuracy and quality of handwritten text recognition.

Sophisticated deep learning technology massively increases the extraction accuracy of handwritten text

Handwritten Text Recognition brings valuable benefits to businesses

Thanks to Parashift’s powerful AI with its advanced deep learning technology, businesses can benefit from numerous advantages with automated handwritten text recognition:

  • Fast, accurate and quality processing of handwritten documents, even when the quality is only moderate
  • Relevant handwritten text can be turned into digital and actionable data
  • Increase straight-through processes and reduce human interactions
  • Process efficiency and customer satisfaction can be improved

With HTR based on deep learning technology, significantly higher straight-through processes are possible