Digitization of paper documents is the first step in an organization's digital transformation journey. It involves scanning all paper documents, printed and handwritten, and capturing the information into actionable, editable, and searchable data.
Optical Character Recognition (OCR) is the system used to convert input text into the machine-encoded format. Traditional OCR extracts data from scanned images and printed text. But when it comes to recognizing handwritten characters, traditional OCR fails to provide accurate results and requires time-consuming and costly manual human intervention.
The OCR market size will be worth USD 13.38 billion by 2025, with a year-on-year growth of 13.7 %. Rapid digitization of business processes using OCR will drive this growth. However, Handwritten Text Recognition (HTR) is a technical challenge for traditional OCR technology. The large variation in character shapes of handwriting styles or cursive fonts poses hurdles to converting it into machine-readable text. For many sectors, recognizing and accurately converting handwritten documents is a crucial problem to solve if they want to achieve their digital transformation goals.
Let's look at some such sectors which deal with handwritten text in critical documents:
- Banking: Handwritten checks are still in use by many customers. If OCR-powered check scanners cannot accurately recognize the handwriting, it lengthens processing times and results in a drop in customer satisfaction.
- Insurance: Even today, we usually fill out insurance claim forms by hand. If OCR does not produce accurate results, these forms must be processed manually, making the process time-consuming and expensive.
- Healthcare: Many doctors still write out prescriptions by hand—and a doctor's handwriting is infamous for its lack of readability. In the age of EHR and digitized medical records, prescriptions that are not readable by computer systems make processing tedious and slow.
- Libraries: Legacy handwritten texts, historical writings, and valuable old information in libraries need preservation. If traditional OCR technology cannot translate these accurately into machine-readable text, then we may lose out on irreplaceable information from the past.
Handwritten Text Recognition (HTR) is becoming an essential aspect of automated data extraction. Powerful AI/ML (Artificial Intelligence/Machine Learning) technology effectively and accurately extracts data from handwritten text.
Sometimes a document is entirely in handwritten text, and at other times, a document contains printed text annotated with important handwritten notes. In either case, the failure of traditional OCR software to read handwriting creates hurdles to digital transformation. It requires time-consuming and expensive human interventions to translate the writing into machine-readable text.
What's wrong with handwriting: Challenges in HTR
Human handwriting is ambiguous. It varies considerably from person to person, and this variability poses significant challenges for the capture and extraction of handwritten text.
- Every person writes with a different slant, with inconsistencies in the shape of each alphabet and different curvatures of alphabets.
- The handwriting style of an individual also varies from time to time and is inconsistent across different writing sessions.
- Technology depends on set rules: most OCR software is designed to recognize text printed in straight lines on white paper. But people don't necessarily write in a straight line on white paper! Sometimes handwriting merges across lines and may be written on a background that disturbs the OCR process.
- We consider cursive handwriting as beautiful—but it's not appreciated much by software developers! It makes the separation and recognition of characters challenging and gives software developers a bit of a headache!
- Printed text (which OCR software loves) is printed straight, but people usually write with a slant rather than straight up.
- If a paper document is damaged or degraded, it may not produce a good quality scan that enables the character recognition software to deliver an accurate conversion.
When you use traditional OCR software for handwritten documents, you will need human intervention to correct the characters that are not accurately recognized by the software—and yes, it is as painful as it sounds!
- It is not easy to source out people who are willing to carry out this tedious, mundane task of correcting badly OCR-ed text and manually extract handwriting to computer-typed text.
- There is a to-and-fro between OCR-converted text and human typing or correction. Many of the sectors that need HTR deal with highly sensitive or personal information. There may also be tight regulatory controls on data privacy in some industries, such as financial services, government and healthcare organizations.
Clearly, recognizing handwritten text needs more advanced technology than conventional OCR.
How does ICR (Intelligent Character Recognition) work?
Intelligent Character Recognition (ICR) is also popularly called Handwriting OCR—it leverages machine learning models that can be trained in handwriting recognition and advanced computer vision engines to 'read' handwritten characters, precisely like a human being can accurately read different styles of handwriting.
AI technology using deep learning algorithms to 'learn' the different variations in different handwriting styles. It means that every time the software encounters a new type of data, it upgrades its recognition database automatically. With each dataset accumulated, artificial intelligence helps the system become predictive when it encounters other datasets.
Benefits of automated ICR or HTR solutions
ICR is an electronic data capture technology that helps you transfer information from hand-filled forms, applications, images and handwritten records. Since it is much less labour-intensive than manual data entry, automated data capture solutions using ICR technology are highly cost-effective.
Businesses benefit in many ways from using intelligent data capture solutions for handwritten text recognition:
- ICR makes your Enterprise Content Management (ECM) system comprehensive and more effective, as handwritten information is extracted accurately and is made actionable.
- It eliminates manual data entry processes and increases the accuracy of the information entered.
- It frees up human resources from tedious, mundane tasks of text corrections and saves costs.
- It speeds up data extraction as no manual intervention is needed.
- It produces accurate results even if the quality of scanned images is poor.
- It creates process efficiency and helps improve customer satisfaction.
DRS offers affordable, intelligent OCR technology, ICR, and OMR (Optical Mark Recognition) electronic data capture services. We can extract electronic information from paper documents, microfilm, microfiche and aperture cards for proper document management and storage.
Our intelligent data capture solutions leverage the power of AI/ML to transform your content management and document processing.
Reach out to us to understand how intelligent data capture can improve the efficiency of your ECM system and save you time and money!