Recognition of Mathematical Notation

Blostein, Dorothea; Grbavec, Ann

doi:10.1142/9789812830968_0021

Cited by 105 publications

(40 citation statements)

References 22 publications

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“…Mathematical expression recognition is an active research field, both for on-line and off-line data [4], [5], [6], Groundthruthed dataset are now available (e.g. [7] (off-line) and [8] (online)).…”

Section: Previous Work In Evaluating Math Recognitionmentioning

confidence: 99%

Stroke-Based Performance Metrics for Handwritten Mathematical Expressions

Zanibbi

Pillay

Mouchère

et al. 2011

2011 International Conference on Document Analysis and Recognition

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Abstract-Evaluating mathematical expression recognition involves a complex interaction of input primitives (e.g. pen/finger strokes), recognized symbols, and recognized spatial structure. Existing performance metrics simplify this problem by separating the assessment of spatial structure from the assessment of symbol segmentation and classification. These metrics do not characterize the overall accuracy of a penbased mathematics recognition, making it difficult to compare math recognition algorithms, and preventing the use of machine learning algorithms requiring a criterion function characterizing overall system performance. To address this problem, we introduce performance metrics that bridge the gap from handwritten strokes to spatial structure. Our metrics are computed using bipartite graphs that represent classification, segmentation and spatial structure at the stroke level. Overall correctness of an expression is measured by counting the number of relabelings of nodes and edges needed to make the bipartite graph for a recognition result match the bipartite graph for ground truth. This metric may also be used with other primitive types (e.g. image pixels).

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Section: Previous Work In Evaluating Math Recognitionmentioning

confidence: 99%

Stroke-Based Performance Metrics for Handwritten Mathematical Expressions

Zanibbi

Pillay

Mouchère

et al. 2011

2011 International Conference on Document Analysis and Recognition

Self Cite

View full text Add to dashboard Cite

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“…Unfortunately, the above attempts did not give details about the classification task † ; they gave only the total performance of the system and specified neither quantitative nor † Moreover, in other attempts [9]- [13], the detail of structure part is completely concealed as a black box of a large math OCR system. qualitative analysis of their results.…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Spatial Relationships among Mathematical Symbols Using Geometric Features

Aly

Uchida

Suzuki

2009

IEICE Trans. Inf. & Syst.

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SUMMARYMachine recognition of mathematical expressions on printed documents is not trivial even when all the individual characters and symbols in an expression can be recognized correctly. In this paper, an automatic classification method of spatial relationships between the adjacent symbols in a pair is presented. This classification is important to realize an accurate structure analysis module of math OCR. Experimental results on very large databases showed that this classification worked well with an accuracy of 99.525% by using distribution maps which are defined by two geometric features, relative size and relative position, with careful treatment on document-dependent characteristics.

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“…Kanahori and Suzuki propose a method for doing this by inserting L A T E X into .pdf files produced by a commercial Optical Character Recognition (OCR) system [1], after passing detected math regions through the Infty math recognition system [2]. A number of methods for recognizing typeset and handwritten mathematics have been devised, and surveys on the subject are available [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Math Spotting: Retrieving Math in Technical Documents Using Handwritten Query Images

Zanibbi

2011

2011 International Conference on Document Analysis and Recognition

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Abstract-A method for locating mathematical expressions in document images without the use of optical character recognition is presented. An index of document regions is produced from recursive X-Y trees produced for each page in the corpus. Queries are provided as images of handwritten expressions, for which an X-Y tree is computed. During retrieval, the query is looked up in the document region index using features of its X-Y tree, producing a set of candidate regions. Candidate regions are ranked by the similarity of vertical pixel projections in their upper and lower halves with those of the query image, as computed using Dynamic Time Warping of the image columns. In an experiment, ten participants each wrote twenty queries from a 200-page corpus. On average, the top-10 retrieval candidates included a candidate covering 43.3% of the test query image (σ = 14.0), with the correct page being returned between 30.0% and 85.0% of the time across participants (µ = 63.2%, σ = 14.9%). When testing using the original query images, 90.0% of the queries were retrieved correctly.

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Recognition of Mathematical Notation

Cited by 105 publications

References 22 publications

Stroke-Based Performance Metrics for Handwritten Mathematical Expressions

Stroke-Based Performance Metrics for Handwritten Mathematical Expressions

Automatic Classification of Spatial Relationships among Mathematical Symbols Using Geometric Features

Math Spotting: Retrieving Math in Technical Documents Using Handwritten Query Images

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