International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.11, No.6, Jun. 2019

Identification of Handwritten Complex Mathematical Equations

Full Text (PDF, 636KB), PP.45-53

Views:3   Downloads:0


Sagar Shinde, Ritu Khanna, Rajendra Waghulade

Index Terms

Neural network;morphological segmentation;recognition;complex equations; template matching


The mathematical notation is well known and used throughout the world. Humanity has evolved from simple methods to represent accounts to the current formal notation capable of modeling complex problems. In addition, mathematical equations are a universal language in the scientific world, and many resources such as science and engineering technology, medical field also not an exception containing mathematics have been created during the last decades. However, to efficiently access all that information, scientific documents must be digitized or produced directly in electronic formats.
Although most people are able to understand and produce mathematical information, introducing mathematical equations into electronic devices requires learning special notations or using editors. The proposed methodology is focused on developing a method to recognize intricate handwritten mathematical equations. For pre-processing, Gray conversion and Weiner filtering are used. Segmentation is performed using the morphological operations, which increase the efficiency of the subsequent image of equation. Finally Neural Network based template matching technique is used to recognize the image of handwritten mathematical equation. 

Cite This Paper

Sagar Shinde, Ritu Khanna, Rajendra Waghulade, "Identification of Handwritten Complex Mathematical Equations", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.6, pp. 45-53, 2019.DOI: 10.5815/ijigsp.2019.06.06


[1]Álvaro, Francisco, Joan-Andreu Sánchez, and José-Miguel Benedí. "Classification of on-line mathematical symbols with hybrid features and recurrent neural networks." In Document analysis and recognition (icdar), 2013 12th international conference on, pp. 1012-1016. IEEE, 2013.

[2]Chaturvedi, Soni, Rutika N. Titre, and NehaSondhiya. "Review of handwritten pattern recognition of digits and special characters using feed forward neural network and Izhikevich neural model." In Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on, pp. 425-428. IEEE, 2014.

[3]Le, AnhDuc, Truyen Van Phan, and Masaki Nakagawa. "A system for recognizing online handwritten mathematical expressions and improvement of structure analysis." In Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on, pp. 51-55. IEEE, 2014.

[4]Liu, Chen, Lina Zuo, Xinfu Li, and Xuedong Tian. "An improved algorithm for Identifying Mathematical formulas in the images of PDF documents." In Progress in Informatics and Computing (PIC), 2015 IEEE International Conference on, pp. 252-256. IEEE, 2015.

[5]Dai Nguyen, Hai, AnhDuc Le, and Masaki Nakagawa. "Deep neural networks for recognizing online handwritten mathematical symbols." In Pattern Recognition (ACPR), 2015 3rd IAPR Asian Conference on, pp. 121-125. IEEE, 2015.

[6]Khatri, Sunil Kumar, Shivali Dutta, and PrashantJohri. "Recognizing images of handwritten digits using learning vector quantization artificial neural network." In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2015 4th International Conference on, pp. 1-4. IEEE, 2015.

[7]Katiyar, Gauri, and ShabanaMehfuz. "MLPNN based handwritten character recognition using combined feature extraction." In Computing, Communication & Automation (ICCCA), 2015 International Conference on, pp. 1155-1159. IEEE, 2015.

[8]Katiyar, Gauri, and ShabanaMehfuz. "SVM based off-line handwritten digit recognition." In India Conference (INDICON), 2015 Annual IEEE, pp. 1-5. IEEE, 2015.

[9]Chajri, Yassine, AbdelkrimMaarir, and BelaidBouikhalene. "A comparative study of handwritten mathematical symbols recognition." In Computer Graphics, Imaging and Visualization (CGiV), 2016 13th International Conference on, pp. 448-451. IEEE, 2016.

[10]Chajri, Yassine, and BelaidBouikhalene. "Handwritten Mathematical Expressions Recognition." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 5 (2016): 69-76.

[11]Ferdinand van der Heijden, “Image Based Measurement Systems, Object Recognition and Parameter Estimation”, John Wiley &Sons, West Sussex, England, 1995.

[12]Gardner, Matt W., and S. R. Dorling. "Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences." Atmospheric environment 32, no. 14-15 (1998): 2627-2636.

[13]Plamondon, Réjean, and Sargur N. Srihari. "Online and off-line handwriting recognition: a comprehensive survey." IEEE Transactions on pattern analysis and machine intelligence 22, no. 1 (2000): 63-84.

[14]Mohamed Cheriet, Mounim A. El-Yacoubi, Hiromichi Fujisawa, Daniel P. Lopresti, and Guy Lorette, "Handwriting recognition research: Twenty years of achievementand beyond," Pattern Recognition, vol. 42, pp. 3131-3135, 2009.

[15]Viard-Gaudin, Christian, Pierre-Michel Lallican, and Stefan Knerr. "Recognition-directed recovering of temporal information from handwriting images." Pattern Recognition Letters 26, no. 16 (2005): 2537-2548.

[16]Chen, Qing. "Evaluation of OCR algorithms for images with different spatial resolutions and noises." PhD diss., University of Ottawa (Canada), 2004.

[17]Rhee, TaikHeon, and Jin Hyung Kim. "Efficient search strategy in structural analysis for handwritten mathematical expression recognition." Pattern Recognition 42, no. 12 (2009): 3192-3201.

[18]Wang, Xin, Guangshun Shi, and Jufeng Yang. "The understanding and structure analyzing for online handwritten chemical formulas." In Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference on, pp. 1056-1060. IEEE, 2009.

[19]Yuan, Zhenming, Hong Pan, and Liang Zhang. "A novel pen-based flowchart recognition system for programming teaching." In Advances in Blended Learning, pp. 55-64. Springer, Berlin, Heidelberg, 2008.

[20]Feng, Guihuan, Christian Viard-Gaudin, and Zhengxing Sun. "On-line hand-drawn electric circuit diagram recognition using 2D dynamic programming." Pattern Recognition 42, no. 12 (2009): 3215-3223.

[21]Szwoch, Mariusz. "Guido: a musical score recognition system." In Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, vol. 2, pp. 809-813. IEEE, 2007.

[22]Çelik, Mehmet, and BerrinYanikoğlu. "Handwritten mathematical formula recognition using a statistical approach." In Signal Processing and Communications  

[23]Applications (SIU), 2011 IEEE 19th Conference on, pp. 498-501. IEEE, 2011.