136 research outputs found
Vision based handwritten character recognition
Cataloged from PDF version of article.Online automatic recognition of handwritten text has been an ongoing research
problem for four decades. It is used in automated postal address and ZIP code and
form reading, data acquisition in bank checks, processing of archived institutional
records, automatic validation of passports, etc. It has been gaining more interest
lately due to the increasing popularity of handheld computers, digital notebooks
and advanced cellular phones. Traditionally, human-machine communication has
been based on keyboard and pointing devices. Online handwriting recognition
promises to provide a dynamic means of communication with computers through
a pen like stylus, not just an ordinary keyboard. This seems to be a more natural
way of entering data into computers.
In this thesis, we develop a character recognition system that combines the
advantage of both on-line and off-line systems. Using an USB CCD Camera,
positions of the pen-tip between frames are detected as they are written on a sheet
of regular paper. Then, these positions are used for calculation of directional
information. Finally, handwritten character is characterized by a sequence of
writing directions between consecutive frames. The directional information of
the pen movement points is used for character pre-classification and positional
information is used for fine classification. After characters are recognized they are
passed to LaTeX code generation subroutine. Supported LaTeX environments are
array construction, citation, section, itemization, equation, verbatim and normal
text environments. During experiments a recognition rate of 90% was achieved.
The main recognition errors were due to the abnormal writing and ambiguity
among similar shaped characters.Öksüz, ÖzcanM.S
Advances in Character Recognition
This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject
An initial evaluation of MathPad(2): A tool for creating dynamic mathematical illustrations
MathPad(2) is a pen-based application prototype for creating mathematical sketches. Using a modeless gestural interface, it lets users make dynamic illustrations by associating handwritten mathematics with free-form drawings and provides a set of tools for graphing and evaluating mathematical expressions and solving equations. In this paper, we present the results of an initial evaluation of the MathPad(2) prototype, examining the user interface\u27s intuitiveness and the application\u27s perceived usefulness. Our evaluations are based on both performance and questionnaire results including first attempt gesture performance, interface recall tests, and surveys of user interface satisfaction and perceived usefulness. The results of our evaluation suggest that, although some test subjects had difficulty with our mathematical expression recognizer, they found the interface, in general, intuitive and easy to remember. More importantly, these results suggest the prototype has the potential to assist beginning physics and mathematics students in problem solving and understanding scientific concepts. (c) 2007 Elsevier Ltd. All rights reserved
Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed
Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition.
In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition
Integrating Multiple Sketch Recognition Methods to Improve Accuracy and Speed
Sketch recognition is the computer understanding of hand drawn diagrams. Recognizing sketches instantaneously is necessary to build beautiful interfaces with real time feedback. There are various techniques to quickly recognize sketches into ten or twenty classes. However for much larger datasets of sketches from a large number of classes, these existing techniques can take an extended period of time to accurately classify an incoming sketch and require significant computational overhead. Thus, to make classification of large datasets feasible, we propose using multiple stages of recognition.
In the initial stage, gesture-based feature values are calculated and the trained model is used to classify the incoming sketch. Sketches with an accuracy less than a threshold value, go through a second stage of geometric recognition techniques. In the second geometric stage, the sketch is segmented, and sent to shape-specific recognizers. The sketches are matched against predefined shape descriptions, and confidence values are calculated. The system outputs a list of classes that the sketch could be classified as, along with the accuracy, and precision for each sketch. This process both significantly reduces the time taken to classify such huge datasets of sketches, and increases both the accuracy and precision of the recognition
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