|
BACK TO
INDEX >>
INTELLIGENT RECOGNITION TECHNOLOGY - THE FINAL FRONTIER
OCR & ICR - IMT Magazine
(Issue 2 - 06)
By: Parascript
The
basic principle of Parascript® Intelligent Recognition states that
handwriting, when reduced to its most basic components, is essentially
motion, or a series of movements, made by a writing instrument. According to
this theory, any handwriting can be described using elements of a special
description language. The eight elements that make up the trajectories of
all cursive letters (Figure 1 below) form a ring that illustrates the
possible transitions of neighbor elements.
Figure 2 - An example of the letter “d” described using motion theory. The
order of elements in the letter description follows the trajectory of a pen.
Horizontal lines show the vertical position on the image associated with
each element in the letter description.
Principles of Dynamic Intelligent Recognition
Both
OCR
and
ICR
deliver high accuracy when analyzing constrained text (OCR with machine
print and ICR with handprint) but are ineffective when dealing with cursive,
where letters are linked together, and may be poorly written or even
illegible. Consider a situation where the symbol segmentation of an image is
ambiguous. In Figure 3 below, an OCR/ICR recognition system could determine
that the first symbol is a “d”or a combination of a “c” and an “l”.
Depending on the segmentation, the reading result produced by a letter-based
recognition technology may be completely different: “clear” in the first
case and “dear” in the second.
As
accurate character segmentation is critical, Intelligent Recognition can
often recognize poor-quality text that would be impossible for
OCR
and
ICR
systems to recognize. Intelligent Recognition dynamically uses context – in
a process similar to the one humans employ when reading and interpreting
text – to compensate for the inherent ambiguity of human handwriting. The
context is used during the recognition process rather than after
recognition, when results might already have been misinterpreted, thus
improving the accuracy of results. Again, going back to Figure 3, it is not
clear if the first symbol is a “d” or a combination of a “c” and an “l”.
The
dynamic vocabularies contained in Intelligent Recognition systems do not
analyze and store all possible hypotheses of segmentation. If the dynamic
vocabulary does not contain a combination of “c” and an “l” at the beginning
of the word, the only possible segmentation solution is “d”. The dynamic
usage of context eliminates all impossible combinations from the solution
set, enabling the evaluation of results “on the fly” during the recognition
process. Dynamic context, therefore, provides the highest possible
recognition accuracy, because it eliminates the impossible results in real
time, during the recognition process.
The Final Frontier
Intelligent Recognition technology often recognizes text that is considered
to be of poor quality or even completely unacceptable for
OCR
and
ICR
technologies, therefore further improving the recognition rates when
compared to other systems. Working with high quality machine print,
OCR
provides recognition accuracy of nearly 100 percent (99.9 %), a level of
accuracy acceptable for many forms processing applications.
ICR
cannot guarantee the same levels of accuracy that
OCR
systems deliver on machine print due to the inherent problems of reading
handprint – spacing variations, diversity of human writing styles, etc.
Instead, state of the art
ICR
systems provide the same recognition accuracy for a certain part of the data
stream, while the data that cannot be reliably read continue to be sent for
visual verification. The following mechanism is used by ICRs to ensure the
accuracy required by the application. The stream of images is divided into
two parts: those that were recognized reliably with a required accuracy
(accepted), and those for which the system does not guarantee the required
accuracy (rejected). Intelligent Recognition further improves recognition
rates and accuracy when compared to traditional machine print (OCR)
and handprint (ICR)
engines through field recognition and cross-validation of results.
Field Recognition
Intelligent Recognition recognizes a field not a character, and consequently
a whole field is either accepted or rejected. Conversely, in the case of a
rejected field Intelligent Recognition technology additionally provides
information about unreliable characters. Second, the reject mechanism is
tuned so thoroughly that it allows accuracy up to 0.1% for the texts of low
quality.
Cross-validation of Results
Computing power alone is not able to deliver high recognition results
without a human-like recognition approach. Intelligent Recognition employs
the most advanced methods of single character recognition while using
sophisticated algorithms to cross-validate results during the recognition
process. Intelligent Recognition advances the state of recognition
technology, exploiting the strengths and capabilities of its predecessors –
OCR
and
ICR
systems – while eliminating their inherent limitations. Intelligent
Recognition technology delivers highly accurate machine print, handprint and
cursive recognition results, helps eliminate laborious human data entry and
has become a proven solution for a broad range of the most demanding
applications for government posts, commercial mailers, banks and financial
institutions, BPO and data processing centers.
|
