Performance evaluation of a robust method for mathematical expression recognition

Okamoto, Masayuki; Imai, Hiroo; Takagi, Keizaburo

doi:10.1109/icdar.2001.953767

Cited by 30 publications

(14 citation statements)

References 9 publications

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“…The most common class of performance metrics for evaluation of math recognition systems are recognition rates, for complete expressions [29,110,163] and individual symbols [8,29,110,143]. Characterizations of layout structure accuracy have been measured using a variety of metrics; most simply, the number of symbols with the appropriate parent symbol, relationship, and depth in a symbol layout tree ('token placement'), and the number of baselines that contain the correct symbols [163].…”

Section: Evaluation Of Math Recognition Systemsmentioning

confidence: 99%

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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: Evaluation Of Math Recognition Systemsmentioning

confidence: 99%

“…Other metrics provide recall measures for layout structures in a symbol layout tree (e.g. scripting, fractions, limits, roots, and matrices [29,110]). …”

Section: Evaluation Of Math Recognition Systemsmentioning

confidence: 99%

Recognition and retrieval of mathematical expressions

Zanibbi

Blostein

2011

IJDAR

234

112

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“…Recently, some new metrics have been proposed to better characterize errors in baseline structure [35], expression syntax [36], and overall system performance [36].…”

Section: Test Resultsmentioning

confidence: 99%

Recognizing mathematical expressions using tree transformation

Zanibbi

Blostein

Cordy

2002

IEEE Trans. Pattern Anal. Machine Intell.

204

111

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Abstract-We describe a robust and efficient system for recognizing typeset and handwritten mathematical notation. From a list of symbols with bounding boxes the system analyzes an expression in three successive passes. The Layout Pass constructs a Baseline Structure Tree (BST) describing the two-dimensional arrangement of input symbols. Reading order and operator dominance are used to allow efficient recognition of symbol layout even when symbols deviate greatly from their ideal positions. Next, the Lexical Pass produces a Lexed BST from the initial BST by grouping tokens comprised of multiple input symbols; these include decimal numbers, function names, and symbols comprised of nonoverlapping primitives such as "=". The Lexical Pass also labels vertical structures such as fractions and accents. The Lexed BST is translated into L A T E X. Additional processing, necessary for producing output for symbolic algebra systems, is carried out in the Expression Analysis Pass. The Lexed BST is translated into an Operator Tree, which describes the order and scope of operations in the input expression. The tree manipulations used in each pass are represented compactly using tree transformations. The compiler-like architecture of the system allows robust handling of unexpected input, increases the scalability of the system, and provides the groundwork for handling dialects of mathematical notation.

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“…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%