Abstract
In the traditional teaching procedure, the repetitive labor of correcting arithmetic exercise brings huge human costs. To reduce these costs and improve the given teaching efficiency, we propose a novel intelligent arithmetic evaluation system, which can automatically identify the meaning of each arithmetic question and make a reasonable judgment or decision. The designed evaluation system can be divided into two modules with detection and identification. In the detection module, due to the intensive distribution and various formats of arithmetic questions in the test papers, we adopt the MixNet-YOLOv3 network with scale balance and lightweight to achieve speed-accuracy trade-off with the mAP being up to 0.989; In the recognition module, considering the formats of each arithmetic problem are mostly fixed, we employ the CRNN network based on the CTC decoding mechanism to achieve an accuracy being up to 0.971. By the incorporation of two networks, the proposed system is capable of intelligently evaluating arithmetic exercise in mobile devices.
T. Liu, C. Liang, X. Dai are also with Jiangsu Provincial Key Laboratory of Image Processing and Image Communication, Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, and also with Jiangsu Provincial Engineering Research Center for High Performance Computing and Intelligent Processing.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (61001152, 61071091, 31671006, 61572503, 61772286, 61872199, 61872424 and 6193000388), China Scholarship Council.
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Liu, T., Liang, C., Dai, X., Luo, J. (2021). Arithmetic Evaluation System Based on MixNet-YOLOv3 and CRNN Neural Networks. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_31
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