2D Shape Detection for Solving Geometry Word Problems

Abstract

The increasing scale of education has decreased the teacher–student ratio, making it difficult to keep track of student's progress individually. Therefore, teachers are leveraging artificial intelligence tools such as smart tutoring digital tools like learning management systems, and automated solvers for various subjects. Mathematics is one of the most fundamental subjects in school education. This work deals with facilitating the automatic solving of geometry math word problems (MWPs) at the elementary school level. To solve such problems, the information from the text as well as the accompanying figure needs to be interpreted. The preliminary task in solving such problems is to first identify which geometric shape is given in the question. Most existing systems or geometry solvers give the shape as one of the inputs to their systems to solve it further. In this work, the focus is on detecting the shape automatedly and proposing a technique for the same using traditional image processing methods. In addition, further, the triangles and quadrilaterals are classified into fine-grained classifications by taking into account their geometrical properties. Performance metrics like precision, recall, accuracy, and F1-score are used to present the results of this approach. The testing is done on various datasets that are readily available. The results show that on Geometry3K, GeoQA, and GeoS datasets the proposed technique attains an accuracy of 77.8%, 93.38%, and 95.02% and an F1 score of 60%, 71%, and 91.97% respectively. Like all other techniques, our system also has a limitation in that it does not detect all inscribed shapes.