that squiggle is a huge business opportunity|
Katiyar / New Delhi November 27, 2008, 1:02
Around $70 billion is spent globally on handwriting
records; scanning, storing and ‘reading’ them requires
handwriting recognition software.
How often have you looked at your meeting or study notes
and given up saying, “Aaargh! I can’t figure out what I
wrote?” Now think of this: How often have you scanned your
handwritten notes desperately looking for a particular name,
word or figure? Most of us do this fairly often in order to
locate details of a particular meeting, a study class,
someone’s phone number taken down in a hurry, a to-do list,
process steps for a task or just a handy recipe dictated by
While we don’t think of decoding handwriting as a task, it
is one we do each day. Combined with this is the act of
“searching” through handwritten documents, looking for data,
ideas, names, places, etc.
Can scanning handwriting and locating critical words be
made easier? Can computers be used for the task? And, more
importantly, are there areas in which computers need to be
used for the task? A hospital administrator will answer that
question fairly fast. Show him a reliable solution to
handwriting recognition and he will beg for it. In hospitals
across the world, doctors, nurses, technicians and
administrative staff fill millions of forms each day – and in
many cases, once filled, they are difficult to read or scan
for critical data. In many instances, the inability to do this
can become a threat to a patient’s life.
Similarly, can a railway clerk scan thousands of forms
looking for handwritten data without making a mistake? Can
handwritten insurance claim forms be read clearly and scanned
for critical data? Can FIRs in a police station be scanned
quickly for important material? Can students share their notes
in a manner that can be understood by other classmates and
About $ 70 billion is spent globally each year on
handwritten forms of the kind used for medical records,
insurance, police complaints, railway reservations, university
work, employee records, invoices and financial details alone.
In many cases, to meet statutory legal requirements the
handwritten record needs to be scanned and stored
“The amount of paper that needs ‘reading' is huge,” says
Thomas Binford, the founder, Chairman and Chief Technology
Officer of Read-Ink, a Bangalore-based start up that has been
developing handwriting recognition software for the last eight
years. Binford, who has supervised more than 40 theses at
Stanford University, while leading research in computer
vision, artificial intelligence, medical image processing,
robotics and industrial inspection, has been a research
scientist at MIT and a Fulbright Scholar at TIFR, Mumbai.
Today, with the help of top-flight Indian talent, Binford
is on the threshold of prototyping his solution to the problem
of handwriting recognition, making any squiggle readable,
searchable and shareable. The most common understanding we
have of handwriting and character recognition is “ORC” or
Optical Character Recognition — where a machine scans
handwriting or text, examines it, matches it against patterns
stored in its memory and spews it out as electronic text which
can be stored, displayed, searched, edited and printed using a
The problem is that ORC has only 95 per cent accuracy – and
that’s an accuracy rate not acceptable in areas such as
medicine, insurance, finance and banking. “At the moment, our
product is able to achieve 98 per cent accuracy,” says Ione
Binford, CEO of Read-Ink, “Our target is 99 per cent.” The
Read-Ink system makes ambiguous guesses about characters and
then makes a lexical match, it self corrects, learns with each
use and becomes better. “People have different ways of forming
characters and no two people are the same,” says Ione Binford.
“Some people use shorthand that only they understand. Our
system is geared to manage all these variants.”
“Only toilet paper is uniform in size and structure. But
take the case of invoices — every invoice is different,” says
Thomas Binford. This is a critical and valuable market to
address. Put another way, the big market to address is the
On the other hand, the device that will lend itself the
most to such hand writing recognition technology is the mobile
phone. With mobile screens becoming touch sensitive, the input
for natural writing is available. Now, you just need software
that can recognise your squiggle, turn it into text and send
it to your bank or to your passport officer.
Although there are several methods and devices that address
the need for handwriting recognition, countries such as India,
China, Brazil and many African nations will leapfrog these
expensive solutions and adopt the answer that becomes
available on cheap mobile phones.
Read-Ink thinks its technology has arrived at just the
right time to encash that need – clearly, it has read the
writing on the wall.
Brief history of handwriting
CalliGrapher: The first two commercially
available PDAs that used handwriting recognition, Apple
MessagePad and Tandy Zoomer, used CalliGrapher. High
expectations and low performance killed the product.
IBMs ThinkWrite: Used character patterns,
strokes and timing to recognize handwriting – Met with limited
Graffiti: A Palm Computing product that
found success because it completely sidestepped the problem of
handwriting recognition. Instead, it forced users to write
characters using certain strokes. Brilliant, but people still
want to use “natural” writing. CEDAR Penman:
An early system that tried to read naturally-written
handwriting using an algorithm based on visual clues, running
them over a neural network trained to figure out words.
OCR: Optical Character Recognition, the
most common form of making text machine readable, storable and
MyScript: Uses a smart pen and paper to
take down notes electronically and records the audio as well.
The notes can be stored in a computer and made searchable and
sharable using OCR.
Others: There are several pen and paper
based devices priced between $100 and $150 that use scanning
techniques to store handwriting and read it for further
processing. None are as accurate as we want them to be.
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