How to extract handwritten text in 2021?
Let’s look at things realistically: A large proportion of documents and forms still come with little to a lot of handwritten text. This will remain the case for the time being. Without the appropriate technology for handwritten text recognition (HTR), this inevitably leads to manual interactions, slow process turnaround times, and bottlenecks.
Parashift platform uses advanced Deep Learning technologies for handwritten text recognition. AI-based Intelligent Character Recognition (ICR) enables automated capture, extraction, and processing of a high variation and complexity of handwriting. Leverage the holy grail of handwritten text recognition and instantly increase your straight-through processing.
In a nutshell – How does handwritten text recognition work?
- AI-based Intelligent Character Recognition (ICR) for more intelligence, instead of weak OCR or traditional ICR
- Pixel identification to find out what the characters are (e.g. what kind of letter or number)
- A mix of different Deep Learning architectures ensures wide coverage of handwriting and best results
- The ICR machine benefits from the high variation of handwriting, which trains it to make more and more complex decisions
How it works: How is handwritten text extracted?
Intelligent Character Recognition (ICR)
Parashift uses Intelligent Character Recognition (ICR), which is based on powerful Artificial Intelligence and advanced Deep Learning technologies. The capabilities go far beyond those of traditional ICR. The powerful ICR with solid capabilities is also bitterly needed when it comes to the extraction of handwritten text. With more handwriting extracted, the machine is trained, accuracy and results are improved.
In order for Parashift ICR to identify what kind of letter, number or character it is, the machine pulls the pixels together and identifies them. With the Deep Learning approach, a neural network is fed a large data set and trained. Deep Learning technologies traverse the immense dataset, detect patterns and decide on the basis of these for an accurate extraction of the handwritten text. With the combination of different Deep Learning architectures, a wide range of handwriting can thus be covered.
Differentiation of handwritten to typed text
The differences in the extraction and processing of handwritten and typed text are great:
- Extremely large variation in different handwritings
- Not only block letters, but also cursive handwriting
- Often very poor quality of handwriting and additional background noise in the forms
Want more details on Handwritten Text Recognition (HTR) and the different Deep Learning architectures? Read more here
5 benefits with Parashift for handwritten text recognition
With Parashift’s approach to handwritten text recognition, complex documents and forms can finally be processed. This brings 5 tremendous benefits to companies:
- You can now generate actionable data from valuable data
- Significantly fewer manual interactions required
- Free up your staff and put them to work where they can make a bigger difference
- Optimize entire workflows
- Boost productivity and increase your straight-through processing (STP)