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Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers

Yordanka Karayaneva and Diana Hintea
Faculty of Engineering, Environment and Computing, Coventry University, Coventry, United Kingdom

Abstract—Schools in many parts of the world use robots as social peers in order to interact with children and young students for a rich experience. Such use has shown significant enhancement of children’s learning. This project uses the humanoid robot NAO which provides object recognition of colours, shapes, typed words, and handwritten digits and operators. The recognition of typed words provides performance of the corresponding movements in the sign language. Five classifiers including neural networks are used for the handwritten recognition of digits and operators. The accuracy of the object recognition algorithms are within the range of 82%-92% when tested on images captured by the robot including the movements which represent words in the sign language. The five classifiers for handwritten recognition produce highly accurate results which are within the range of 87%-98%. This project will serve as a promising provision for an affective touch for children and young students.

Index Terms—object recognition, image processing, machine learning, robotics, visual learning

Cite: Yordanka Karayaneva and Diana Hintea, "Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers," Journal of Image and Graphics, Vol. 6, No. 1, pp. 10-20 June 2018. doi: 10.18178/joig.6.1.10-20


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