Math-recognition

This repository summaries publications on Recognition of Handwritten Mathematical Expressions

A list of all papers on Recognition of Handwritten Mathematical Expressions. This repository is maintained by Anh Duc Le. To complement or correct it, please contact me via email : leducanh841988@gmail.com.

1. Survey paper

• K. Chan and D. Yeung, Mathematical expression recognition: A Survey, International Journal of Document Analysis and Recognition, pp. 3-15, 2000.
• R. Zanibbi and D. Blostein, Recognition and retrieval of mathematical expressions, International Journal of Document Analysis and Recognition, pp. 331-357, 2012.

2. Symbol Segmentation

• Lehmberg, S., Winkler, H.J., Lang, M.: A soft-decision approach for symbol segmentation within handwritten mathematical expressions. In: Proceeding of international conference on acoustics, speech, and signal processing, vol. 6, pp. 3434–3437. Atlanta (1996)

• Toyozumi, K., et al.: A study of symbol segmentation method for handwritten mathematical formula recognition using mathematical structure information. In: Proceedings of the 17th ICPR, vol. 2, pp. 630–633. Cambridge (2004)

• Shi, Y., Li, H., Soong, F.K.: A unified framework for symbol segmentation and recognition of handwritten mathematical expressions. In: Proceedings of the 9th ICDAR, vol. 2, pp. 854–858. Curitiba (2007)

• Hu, L., Zanibbi, R.: Segmenting handwritten math symbols using adaboost and multi-scale shape context features. In: Proceedings of the 12th ICDAR, pp. 1180–1184. Washington (2013)

3. Symbol Recognition

• MacLean, S., Labahn, G.: Elastic matching in linear time and constant space. In: Proceedings of the 9th IAPR Workshop on Document Analysis Systems. (2010)

• Hu, L., Zanibbi, R.: HMM-based recognition of online hand-written mathematical symbols using segmental K-means initialization and a modified pen-up/down feature. In: Proceedings of the 11th ICDAR, pp. 457–462. Beijing (2011)

• Álvaro, F., Sánchez, J. A., Benedí, J. M.: Classification of on-line mathematical symbols with hybrid features and recurrent neural networks. In: Proceedings of the 12th ICDAR, pp. 1012–1016. Washington (2013)

• Davila, K.M., Ludi, S., Zanibbi R.: Using off-line features and synthetic data for on-line handwritten math symbol recognition. In: Proceedings of the 14th ICFHR, pp. 323–328. Crete (2014)

• Álvaro, F., Sánchez, J.A., Benedí, J.M.: Offline features for classifying handwritten math symbols with recurrent neural networks. In: Proceedings of the 22nd ICPR, pp. 2944–2949. Stockholm (2014)

4. Parsing algorithm and full recognition system

• Garain, U., Chaudhuri, B.B.: Recognition of online handwritten mathematical expressions. IEEE Trans. Syst. Man Cybern. B Cybern. 34, 2366–2376 (2004)
• Okamoto, M., Miao, B.: Recognition of mathematical expressions by using the layout structure of symbols. In: Proceedings of the 1st ICDAR, pp. 242–250. Saint Malo (1991)
• Eto, T., Suzuki, M.: Mathematical formula recognition using virtual link network. In: Proceedings of the 6th ICDAR, pp. 762–767. Seattle (2001)
• Zanibbi, R., Blostein, D., Cordy, J.R.: Recognizing mathematical expressions using tree transformation. IEEE Trans. PAMI, 24, 1455–1467 (2002)
• Rhee, T.H., Kim, J.H.: Efficient search strategy in structural analysis for handwritten mathematical expression recognition. Pattern Recogn. 42, 3192–3201 (2009)
• Yamamoto, R., Sako, S., Nishimoto, T., Sagayama, S.: OnLine recognition of handwritten mathematical expressions based on stroke-based stochastic context-free grammar. In: 10th IWFHR, pp. 249–254. La Baule (2006)
• Simistira, F., Katsouros, V., Carayannis, G.: Recognition of online handwritten mathematical formulas using probabilistic SVMs and stochastic context free grammars. Pattern Recogn. Lett. 53, 85–92 (2015)
• MacLean, S., Labahn, G.: A New approach for recognizing handwritten mathematics using relational grammars and fuzzy sets. Int. J. Doc. Anal. Recogn. 16, 139–163 (2013)
• MacLean, S., Labahn, G.: A Bayesian model for recognizing handwritten mathematical expressions. Pattern Recogn. 48, 2433–2445 (2015)
• Álvaro, F., Sánchez, J.A., Benedí, J.M.: Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models. Pattern Recogn. Lett. 35, 58–67 (2014)
• Awal, A.M., Mouchére, H., Viard-Gaudin, C.: A global learning approach for an online handwritten mathematical expression recognition system. Pattern Recogn. Lett. 35, 6877 (2014) -Le, A.D., Phan, T.V., Nakagawa, M.: A System for recognizing online handwritten mathematical expressions and improvement of structure analysis. In: Proceedings of the 11th IAPR Workshop on Document Analysis Systems, pp. 51–55. (2014)

5. Databases

• Mouchère, H., Viard-Gaudin, C., Zanibbi, R., Garain, U., Kim, D.H., Kim, J.H.: ICDAR 2013 CROHME: third international competition on recognition of online handwritten mathematical expressions. In: Proceedings of the 12th ICDAR, pp. 1428–1432. (2013)Google Scholar
• Mouchère, H., Viard-Gaudin, C., Zanibbi, R., Garain, U.: ICFHR: competition on recognition of on-line handwritten mathematical expressions (CROHME 2014). In: Proceedings of the 14th ICHFR, pp. 791–796. Crete (2014)Google Scholar
• Le, A.D., Nakagawa, M.: A tool for ground-truthing online handwritten mathematical expressions. In: Proceedings of the 16th International Graphonomics Society Conference, pp. 70–73. (2013)Google Scholar