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Evaluation of Techniques for Visualizing Mathematical Expression Recognition Results
"... We present an experimental study that evaluates four different techniques for visualizing the machine interpretation of handwritten mathematics. Typeset in Place puts a printed form of the recognized expression in the same location as the handwritten mathematics. Adjusted Ink replaces what was writt ..."
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We present an experimental study that evaluates four different techniques for visualizing the machine interpretation of handwritten mathematics. Typeset in Place puts a printed form of the recognized expression in the same location as the handwritten mathematics. Adjusted Ink replaces what was written with scaledtofit, cleaned up handwritten characters using an ink font. The Large Offset technique scales a recognized printed form to be just as wide as the handwritten input, and places it below the handwritten mathematical expression. The Small Offset technique is similar to Large Offset but the printed form is set to be a fixed size which is generally small compared to the written expression. Our experiment explores how effective each technique is with assisting users in identifying and correcting recognition mistakes with different types and quantities of mathematical expressions. Our evaluation is based on task completion time and a comprehensive postquestionnaire used to solicit reactions on each technique. The results of our study indicate that, although each technique has advantages and disadvantages depending on the complexity of the handwritten mathematics, subjects took significantly longer to complete the recognition task with Typeset in Place and generally preferred
Combining prediction and recognition to improve online mathematical character recognition
, 2006
"... This paper describes methods to increase the accuracy of mathematical handwriting analysis by using context information. Our approach is based on the assumption that likely expression continuations can be derived from a database of mathematical expressions and then can be used to rank the candidates ..."
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This paper describes methods to increase the accuracy of mathematical handwriting analysis by using context information. Our approach is based on the assumption that likely expression continuations can be derived from a database of mathematical expressions and then can be used to rank the candidates of isolated symbol recognition. We present how predicted continuations for an expressions are derived, how they are combined with the recognition candidates, and the effectiveness of the results. We first review the techniques we have used to build and represent a mathematical context database. We then describe different strategies for combining context information with results obtained from the recognition of individual characters. Finally we present a summary of a case study, using a fixed dataset of common mathematical expressions to test the accuracy of online analysis.
Communicating Mathematics via PenBased Interfaces
"... In this work we address the question of how to organize penbased interfaces for mathematical software systems. We describe our approach to such interfaces both for mathematical packages and document processing software. Our architecture includes components for ink collection, mathematicallyoriented ..."
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In this work we address the question of how to organize penbased interfaces for mathematical software systems. We describe our approach to such interfaces both for mathematical packages and document processing software. Our architecture includes components for ink collection, mathematicallyoriented recognizers, portability support and interfaces to applications. We summarize aspects of mathematical handwriting recognition and discuss the methods we have used for individual character recognition and overall expression analysis. We present our penbased computing environment Mathink and give an overview of facilities for training, ink annotation, and testing. 1
Designing UI Techniques for Handwritten Mathematics
"... We discuss the design of user interface techniques for visualizing and controlling the recognition of handwritten mathematics. In particular, we present a range of visualization styles for displaying the result of math recognition. These styles offer different tradeoffs between ease of user correct ..."
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We discuss the design of user interface techniques for visualizing and controlling the recognition of handwritten mathematics. In particular, we present a range of visualization styles for displaying the result of math recognition. These styles offer different tradeoffs between ease of user correction of errors in recognition and impact on the userâ€™s entry of math. We also describe recognition control techniques, including using usercontrolled mappings of allographs to achieve more robust symbol recognition and provide extensions to notation, and UI control of nonspatial information used in recognition. We generally do not discuss the precise user interface implementation necessary to use these techniques, for example whether to use menus or gestures, but just the functionality required. Finally, we provide, in an appendix, a sketch of the recognition and display implementation behind our techniques.