On-line handwritten formula recognition using hidden Markov models and context dependent graph grammars

Kosmala, A.; Rigoll, Gerhard; Lavirotte, Stéphane; Pottier, Loïc

doi:10.1109/icdar.1999.791736

Cited by 41 publications

(17 citation statements)

References 12 publications

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“…Recognition consists in finding the sequence of states with higher probability. Features used to represent the symbols in HMM approaches include discrete cosine transformation [18], log-polar mapping [21] and image pixels [33]. HMMs are able to segment the symbols and to recognize distorted symbols.…”

Section: Structural Symbol Recognitionmentioning

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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Section: Structural Symbol Recognitionmentioning

confidence: 99%

Symbol Recognition: Current Advances and Perspectives

Lladós¹,

Valveny²,

Sánchez³

et al. 2002

Lecture Notes in Computer Science

102

View full text Add to dashboard Cite

show abstract

“…Ashida et al [6] Symbol recognition rate Chan and Yeung [10] Symbol recognition rate Expression recognition rate Operator recognition rate Integrated performance measure Garain and Chaudhuri [12] Global performance index Average performance index Kosmala et al [17] Computing time Okamoto et al [21] Expression recognition rate Character recognition rate Structure recognition rate Takiguchi et al [23] Character recognition rate Zanibbi et al [28] Baseline recognition rate Token placement rate Expression recognition rate…”

Section: Authors Metricsmentioning

confidence: 99%

Issues in Performance Evaluation: A Case Study of Math Recognition

Lapointe

Blostein

2009

2009 10th International Conference on Document Analysis and Recognition

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“…Several methods have been proposed to solve this problem, such as HMM [39,18,16], Neural Networks [33], Elastic Matching [7,36] or Support Vector Machines [17]. Furthermore, some of these proposals combine on-line and off-line information to perform hybrid classification and improving recognition results [39,17].…”

Section: Related Workmentioning

confidence: 99%

Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models

Alvaro

Sánchez

Benedí

2014

Pattern Recognition Letters

134

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This paper describes a formal model for the recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models. Hidden Markov models are used to recognize mathematical symbols, and a stochastic context-free grammar is used to model the relation between these symbols. This formal model makes possible to use classic algorithms for parsing and stochastic estimation. In this way, first, the model is able to capture many of variability phenomena that appear in on-line handwritten mathematical expressions during the training process. And second, the parsing process can make decisions taking into account only stochastic information, and avoiding heuristic decisions. The proposed model participated in a contest of mathematical expression recognition and it obtained the best results at different levels.

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On-line handwritten formula recognition using hidden Markov models and context dependent graph grammars

Cited by 41 publications

References 12 publications

Symbol Recognition: Current Advances and Perspectives

Symbol Recognition: Current Advances and Perspectives

Issues in Performance Evaluation: A Case Study of Math Recognition

Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models

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