CharacTell maintains the
belief that it is possible to break technological barriers in
the area of automated character recognition. We met this goal
by developing Advanced Character Recognition (ACR),
our core technology that superseded capabilities existing technologies,
while opening the door to new applications that previously had
not been feasible.
CharacTell's mission is:To continue to demonstrate technical
leadership in recognition by offering competitive ACR-based products
that would satisfy real customer needs - developers and users
alike - in existing and growth markets.
CharacTell Ltd was founded in 1998 by Mr. Ofer Comay and Dr.
Eliyahu Comay with the goal of developing advanced character recognition
technology that avoided many of the limitations that hindered
the technologies of the time. In creating ACR, the pair - bringing
expertise and many years of mathematical, technical and scientific
experience to the effort - succeeded in not only improving recognition
performance of hand-printed characters over existing products,
but for the first time made recognizing hand-written characters
practical and successful.
In 2001, Dr. Paz Kahana joined CharacTell and became its President
and CEO. Dr. Kahana brings to CharacTell years of expertise in
marketing and management, that are crucial for CharacTell’s
continued growth and development.
Handwriting is one of the most common and natural ways for humans
to communicate. This is true for what we do: notes we take, letters
we write, and documents we produce. ACR uniquely identifies characters
based on concepts equivalent to methods used to analyze the structure
of the human genome. ACR has the ability to learn languages, fonts,
and individual handwriting styles, quickly and completely, using
smaller sample sets than any existing technology. It identifies
characters with accuracy rates resembling the determinism generated
when DNA analysis is used to pinpoint the identity of an unknown
person. An important and strategic feature of ACR is that is learns
and trains not only uniquely, but at a fraction of the time, sample
size and cost that is needed elsewhere.