|
Showing 1 - 2 of
2 matches in All Departments
Over the last few decades, research on handwriting recognition has
made impressive progress. The research and development on
handwritten word recognition are to a large degree motivated by
many application areas, such as automated postal address and code
reading, data acquisition in banks, text-voice conversion,
security, etc. As the prices of scanners, com puters and
handwriting-input devices are falling steadily, we have seen an
increased demand for handwriting recognition systems and software
pack ages. Some commercial handwriting recognition systems are now
available in the market. Current commercial systems have an
impressive performance in recognizing machine-printed characters
and neatly written texts. For in stance, High-Tech Solutions in
Israel has developed several products for container ID recognition,
car license plate recognition and package label recognition. Xerox
in the U. S. has developed TextBridge for converting hardcopy
documents into electronic document files. In spite of the
impressive progress, there is still a significant perfor mance gap
between the human and the machine in recognizing off-line
unconstrained handwritten characters and words. The difficulties
encoun tered in recognizing unconstrained handwritings are mainly
caused by huge variations in writing styles and the overlapping and
the interconnection of neighboring characters. Furthermore, many
applications demand very high recognition accuracy and reliability.
For example, in the banking sector, although automated teller
machines (ATMs) and networked banking sys tems are now widely
available, many transactions are still carried out in the form of
cheques."
This book takes a fresh look at the problem of unconstrained handwriting recognition and introduces the reader to new techniques for the recognition of written words and characters using statistical and soft computing approaches. The types of uncertainties and variations present in handwriting data are discussed in detail. The book presents several algorithms that use modified hidden Markov models and Markov random field models to simulate the handwriting data statistically and structurally in a single framework. The book explores methods that use fuzzy logic and fuzzy sets for handwriting recognition. The effectiveness of these techniques is demonstrated through extensive experimental results and real handwritten characters and words.
|
You may like...
The High Notes
Danielle Steel
Paperback
R340
R266
Discovery Miles 2 660
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.