Understanding Recognition Technology – An Insider’s Guide, dives into the technical aspects of how recognition software works, explains operational details, provides examples, and identifies how and when human involvement is required in the process. Download the full eBook: http://hub.am/1lvNogg

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Understanding Recognition Technology
– An Insider’s Guide
Setting the Right Expectations for a Successful Implementation

2. 2© 2012 Parascript, LLC
eBook
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Presentation
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Agenda
 Understanding the Recognition Process
 Understanding Errors & Rejects
 The Operating Point
 Common Misconceptions
 Context & Business Rules to Get the Most Benefits from Data
 Why Results May Not Meet Expectations
 Conclusion

4. 4© 2012 Parascript, LLC
Introduction
Understanding Recognition Technology – An Insider’s Guide, dives into the technical
aspects of how recognition software works, explains operational details, provides
examples, and identifies how and when human involvement is required in the process.
Companies, organizations and government entities worldwide use recognition technology to
process documents, forms, checks, mail and other important business content with high
speed and accuracy.
Types of Recognition Technology
 Recognize machine print with OCR
(Optical Character Recognition)
 Recognize handwriting, cursive handwriting, check marks, barcodes with ICR
(Intelligent Character Recognition)

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Recognition Process
 The typical recognition process:
 input images are fed to the software.
 After recognition, processed data is divided into two streams:
• accepted answers
• rejected answers.
 After images are accepted, data includes:
• correct answers
• errors

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TRADEOFF BETWEEN ERRORS AND REJECTS
 Depending on application needs, users can either reduce rejects by
increasing error rates or reduce error rates by increasing reject rates.
Less Errors =
More Rejects =
More manual
processing
More Errors =
Less Rejects =
Less manual
processing
required

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Reducing rejects by increasing error rates allows
companies to process more information
automatically, without the need for human
intervention. This lowers processing costs.
“ “

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IMPORTANCE OF THE OPERATING POINT
 Operating Point: a critical number since it is used to build a business case, establish an
ROI, and set the benchmark to measure against
 Example
 Operating Point is set to 85% read rate @ 1% error rate
 100 Documents are processed
 85 Documents will be read by the software properly with an error on 1
 The remaining 15 documents are sent for manual processing
• For comparison, human (manual) processing typically yields a 3% error rate
The operating point is the most
critical number in recognition.“
“ The Rules For Using
Confidence Intervals
Download Understanding
Recognition Technology EBook for
the full story on the importance of
Operating Point
Page 8

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COMMON MISCONCEPTIONS ABOUT RECOGNITION
Two Common Misconceptions about Recognition
all items in a stream have an equal level of difficulty,
regardless if they are processed automatically or
manually
that values should be recognized first and then use
rules and context
1
2
Common misconceptions relate to
the level of recognition difficulty
and the use of context and rules.
“ “

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CONTEXT & BUSINESS RULES TO GET THE MOST BENEFIT FROM DATA
 The effective use of context and business rules are tools that:
 Allows users to process information faster
 reduce reliance on manual data entry and related costs
 derive the most benefits from their data
 Context
 In recognition, context explains the characteristics
or properties of data within a field
 Rules
 AKA “Logic”, rules apply “if/then” thinking or specific
scenarios to ensure that the data meets defined criteria

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WHY RESULTS MAY NOT MEET EXPECTATIONS
 Goal of Recognition
 to produce the highest accuracy possible
 Reality
 type and quality of images being recognized can and often
produces significantly different results
 individual characters, such as in handwriting, are more
ambiguous than others and harder to recognize
 To Achieve Better Results
 Better results from “poor quality” images and hard-to-read
characters, advanced handwriting recognition technology
employs the use of dynamic vocabularies

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Advanced handwriting recognition technology
employs the use of dynamic vocabularies to produce
better results on poor quality images and hard-to-
read characters.
“ “

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CONCLUSION
 Companies and organizations with complex high volume processing requirements
benefit from automated recognition solutions.
 Recognition technology has advanced substantially.
 Current software options include sophisticated image analysis and pattern recognition
algorithms.
 Businesses want to gain the most value for their software by achieving the highest
possible accuracy rates accompanied by lower costs.
 Recognition Best Practices require that you understand:
 Error and reject rates
 confidence
 operating point
 context and business rules to set criteria to improve accuracy

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Understanding Recognition Technology – An Insider’s Guide