Using Off-Line Features and Synthetic Data for On-Line Handwritten Math Symbol Recognition

Davila, Kenny; Ludi, Stephanie; Zanibbi, Richard

doi:10.1109/icfhr.2014.61

Cited by 35 publications

(18 citation statements)

References 22 publications

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“…In this methodology, offline features were converted to on-line counterpart by characterizing the shapes of the mathematical symbols. They have achieved a significant accuracy of 89.87% on MathBrush dataset [8].…”

Section: Review Of Literaturementioning

confidence: 98%

A Simplified Research for Mathematical Expression Recognition and Its Conversion to Speech

2019

IJRTE

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The number of visually impaired people appearing for various examination is increasing every year while on the other hand, there are several blind aspirants who are willing to enrich their knowledge through higher studies. Mathematics is one of the key language (subject) for those who are willing to pursue higher studies in science stream. There is a lot of advanced Braille techniques and OCR to speech conversion software's made available to help visual impaired community to pursue their education but still the number of visually impaired students getting admitted to higher education is less. This is not because most of the data is on paper in the form of books and documents. So, there is a great need to convert information from the physical domain into the digital domain which would help the visually impaired people to read the advanced mathematics text independently. Optical Character Recognition (OCR) systems for mathematics have received considerable attention in recent years due to the tremendous need for the digitization of printed documents. Existing literature reveals that, most of the works concentrated on recognizing handwritten mathematical symbols and some works revolve around complex algorithms. This paper proposes a simple, yet efficient approach to develop an OCR system for mathematics and its conversion to speech. For Mathematical symbol recognition, Skin and Bone algorithm is proposed, which proved its efficiency on a variety of data set. The proposed methodology has been tested on 50 equations comprising various symbols such as integral, differential, square, square root and currently achieving recognition rate of 92%.

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Section: Review Of Literaturementioning

confidence: 98%

A Simplified Research for Mathematical Expression Recognition and Its Conversion to Speech

2019

IJRTE

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“…30,000 formulae). RIT used a synthetic data generation technique to produce a training set five times larger than the original for Task 2 [5]. All participants used the validation data sets to prevent over-fitting by machine learning algorithms and/or model selection.…”

Section: A Datamentioning

confidence: 99%

ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions

Mouchère

Viard-Gaudin

Zanibbi

et al. 2016

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)

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This paper presents an overview of the 5th Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME). As in previous years, the main task is formula recognition from handwritten strokes (Task 1). Additional tasks include classification of isolated symbols (Task 2a), classification of isolated valid and invalid symbols (Task 2b), a new task on parsing formula structure from valid handwritten symbols (Task 3), and parsing expressions with matrices (Task 4, experimental). In total, eleven (11) research labs registered for the competition, with six (6) teams submitting results. Innovations for this CROHME included providing a corpus of formulae from Wikipedia to train language models, and an online system for result submission. The highest recognition rates were obtained by MyScript corporation (Task 1. 67.65%, 2a. 92.81%, 2b. 86.77%, 3. 84.38%, and 4. 68.40%). Using only provided training data, the highest recognition rates were obtained by WIRIS corporation (Task 1. 49.61%, Task 3. 78.80%, Task 4. 56.40%), the Tokyo University of Agriculture and Technology (Task 2a. 92.28%), and RIT (Task 2b. 83.34%). The competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task.

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“…5 Task 1. The symbol classifier by K. Davila is an SVM with a Gaussian Kernel trained for probabilistic classification [10] using a combination of on-line and off-line features. Online features include normalized line length, number of strokes, covariance of point coordinates, and the number of points with high variation in curvature and total angular variation.…”

Section: Rochester Institute Of Technology Drpl (System Iv)mentioning

confidence: 99%

ICFHR 2014 Competition on Recognition of On-Line Handwritten Mathematical Expressions (CROHME 2014)

Mouchère

Viard-Gaudin

Zanibbi

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

104

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We present the outcome of the latest edition of the CROHME competition, dedicated to on-line handwritten mathematical expression recognition. In addition to the standard full expression recognition task from previous competitions, CROHME 2014 features two new tasks. The first is dedicated to isolated symbol recognition including a reject option for invalid symbol hypotheses, and the second concerns recognizing expressions that contain matrices. System performance is improving relative to previous competitions. Data and evaluation tools used for the competition are publicly available.

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Using Off-Line Features and Synthetic Data for On-Line Handwritten Math Symbol Recognition

Cited by 35 publications

References 22 publications

A Simplified Research for Mathematical Expression Recognition and Its Conversion to Speech

A Simplified Research for Mathematical Expression Recognition and Its Conversion to Speech

ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions

ICFHR 2014 Competition on Recognition of On-Line Handwritten Mathematical Expressions (CROHME 2014)

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