Utilizing Consistency Context for Handwritten Mathematical Expression Recognition

Kim, Kang; Rhee, Taik Heon; Lee, Jae Seung; Kim, Jin Hyung

doi:10.1109/icdar.2009.140

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…For example, Kim et al [73] modify the penalty metric used in an A* search for constructing symbol layout trees for handwritten expressions [122]. The penalty metric considers mea-sures of consistency of symbol size, style, and repetition, along with symbol n-grams and repeated subscripting.…”

Section: Heuristic Rules and Contextual Constraintsmentioning

confidence: 99%

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

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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Section: Heuristic Rules and Contextual Constraintsmentioning

confidence: 99%

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

234

112

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“…Uma das diculdades que tem sido recentemente apontada na área de reconhecimento de expressões matemáticas é a quase inexistência de base de dados pública [3,23]. As bases existentes são, em geral, limitadas quanto ao tipo de símbolos, expressões, quantidade de pessoas e de amostras.…”

Section: Motivaçãounclassified

Rotulação de símbolos matemáticos manuscritos via casamento de expressões

Honda¹

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The problem of recognizing handwritten mathematical expressions includes three important subproblems: symbol segmentation, symbol recognition, and structural analysis of expressions. In order to evaluate recognition methods and techniques, they should be tested on representative sample sets of the application domain. One of the concerns that are being repeatedly pointed recently is the almost non-existence of public representative datasets of mathematical expressions, which makes dicult the development and comparison of distinct approaches. In general, recognition results reported in the literature are restricted to small datasets, not publicly available, and often consisting of data aiming only evaluation of some specic aspects of the recognition. In the case of online expressions, to train and test symbol recognizers, samples are in general obtained asking users to write a series of symbols individually and repeatedly. Such task is boring and tiring. An alternative approach for obtaining samples of symbols would be to ask users to transcribe previously dened model expressions. By doing so, writing would be more natural and less boring, and several symbol samples could be obtained from one transcription. To avoid the task of manually labeling the symbols of the transcribed expressions, in this work a method for handwritten expression matching, in which symbols of a transcribed expression are assigned to the corresponding ones in the model expression, is proposed. The proposed method is based on a formulation that reduces the matching problem to a linear assignment problem, where costs are dened based on symbol features and expression structure. Experimental results using the proposed method show that mean correct assignment rate superior to 99% is achieved.

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A dive in white and grey shades of ML and non-ML literature: a multivocal analysis of mathematical expressions

Sakshi

Kukreja

2022

Artif Intell Rev

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Utilizing Consistency Context for Handwritten Mathematical Expression Recognition

Cited by 8 publications

References 7 publications

Recognition and retrieval of mathematical expressions

Recognition and retrieval of mathematical expressions

Rotulação de símbolos matemáticos manuscritos via casamento de expressões

A dive in white and grey shades of ML and non-ML literature: a multivocal analysis of mathematical expressions

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