System for Recognition and Evaluation of
Handwritten Arithmetic Expressions
Vlado Galić and Tomislav Hrkać
Abstract
Recognition of mathematical expressions today is a very interesting area of application of deep learning. The problem is quite complex and requires a complex system to solve it. The problem becomes even more complex if arithmetic expressions are handwritten. The use of convolutional neural networks yields satisfactory results but leaves some room for improvement. In this paper, a simplified variant of the above problem is solved by using an approach that consists of extraction and classification of individual symbols using convolutional neural networks followed by syntactic parsing of the expression taking into account the symbol positions.
Files
DOI
10.20532/ccvw.2019.0008
https://doi.org/10.20532/ccvw.2019.0008
BibTeX
@InProceedings{10.20532/ccvw.2019.0008, author = {Gali{\' c}, Vlado and Hrka{\' c}, Tomislav}, title = {System for Recognition and Evaluation of Handwritten Arithmetic Expressions}, year = 2018, address = {Zagreb}, month = {October}, organization = {Center of Excellence for Computer Vision}, publisher = {University of Zagreb}, abstract = {Recognition of mathematical expressions today is a very interesting area of application of deep learning. The problem is quite complex and requires a complex system to solve it. The problem becomes even more complex if arithmetic expressions are handwritten. The use of convolutional neural networks yields satisfactory results but leaves some room for improvement. In this paper, a simplified variant of the above problem is solved by using an approach that consists of extraction and classification of individual symbols using convolutional neural networks followed by syntactic parsing of the expression taking into account the symbol positions.}, doi = {10.20532/ccvw.2019.0008}, url = {https://doi.org/10.20532/ccvw.2019.0008} }