User Interfaces for On-Line Diagram Recognition

Blostein, Dorothea; Lank, Edward; Rose, Arlis; Zanibbi, Richard

doi:10.1007/3-540-45868-9_8

Cited by 13 publications

(10 citation statements)

References 24 publications

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“…SketchVis uses eager recognition [1]. This means that a data attribute could be recognized from a single letter if no other data attributes start with that letter.…”

Section: Discussionmentioning

confidence: 99%

Data analysis on interactive whiteboards through sketch-based interaction

Browne

Lee

Carpendale

et al. 2011

Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces

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When faced with the task of understanding complex data, it is common for people to work on whiteboards, where they can collaborate with others, brainstorm lists of important questions, and sketch simple visualizations. However, these sketched visualizations seldom contain real data. We address this gap by extending these sketched whiteboard visualizations with the actual data to be analyzed. Guided by an iterative design process, we developed a better understanding of the challenges involved in bringing sketchbased interaction to data analysis. In this work we contribute insights into the design challenges of sketch-based charting, and we present SketchVis, a system that leverages hand-drawn input for exploring data through simple charts.

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“…SketchVis uses eager recognition [1]. This means that a data attribute could be recognized from a single letter if no other data attributes start with that letter.…”

Section: Discussionmentioning

confidence: 99%

Data analysis on interactive whiteboards through sketch-based interaction

Browne

Lee

Carpendale

et al. 2011

Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces

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“…The existing literature [34,63,72] uses feature vectors to recognize individual symbols and syntactic approaches to validate the structure of the formula. Symbol recognition procedures are also used as a tool for man-machine interfaces [66,81,75,82,83]. This is not only an specific application domain but it also requires specific techniques because the recognition is performed on-line.…”

Section: Other Applicationsmentioning

confidence: 99%

Symbol Recognition: Current Advances and Perspectives

Lladós¹,

Valveny²,

Sánchez³

et al. 2002

Lecture Notes in Computer Science

102

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Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.

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“…optimizing recognition model parameters) -all come into play when recognizing mathematics. The math domain 2 (a) Freehand Formula Entry System (b) XPRESS [116] (c) InftyEditor/InftyReader [141] (FFES) [20,136] (a) MathBrush [81] (b) E-chalk [144] (c) MathPad 2 [85] (d) Li, Zeleznik et al [89] offers sufficient complexity to challenge researchers, yet has characteristics that make the domain tractable: the semantics of math notation are fairly constrained, and a typical math expression consists of relatively few symbols.…”

Section: Introductionmentioning

confidence: 99%

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

Self Cite

234

112

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Document recognition and retrieval technologies complement one another, providing improved access to increasingly large document collections. While recognition and retrieval of textual information is fairly mature, with wide-spread availability of Optical Character Recognition (OCR) and text-based search engines, recognition and retrieval of graphics such as images, figures, tables, diagrams, and mathematical expressions are in comparatively early stages of research. This paper surveys the state of the art in recognition and retrieval of mathematical expressions, organized around four key problems in math retrieval (query construction, normalization, indexing, and relevance feedback), and four key problems in math recognition (detecting expressions, detecting and classifying symbols, analyzing symbol layout, and constructing a representation of meaning). Of special interest is the machine learning problem of jointly optimizing the component algorithms in a math recognition system, and developing effective indexing, retrieval and relevance feedback algorithms for math retrieval. Another important open problem is developing user interfaces that seamlessly integrate recognition and retrieval. Activity in these important research areas is increasing, in part because math notation provides an excellent domain for studying problems common to many document and graphics recognition and retrieval applications, and also because mature applications will likely provide substantial benefits for education, research, and mathematical literacy.

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User Interfaces for On-Line Diagram Recognition

Cited by 13 publications

References 24 publications

Data analysis on interactive whiteboards through sketch-based interaction

Data analysis on interactive whiteboards through sketch-based interaction

Symbol Recognition: Current Advances and Perspectives

Recognition and retrieval of mathematical expressions

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