Recognition of MNIST handwritten digits and character set research
The goal of the work is the study of influence of descriptors and reduction of their quantity for recognition of MNIST database of handwritten digits.
For recognition of the MNIST digits, a set of 12 descriptors was chosen. Statistical analysis of descriptors was performed. Analysis of descriptors gave the reason to assume, that the fifth, sixth and seventh Hu-moments doesn’t contribute into result of digit recognition. Digit recognition with usage of classifier based on on k-means method with n_neighbors = 10 of Scikit-Learn Python system library was done. Best results using 8 descriptors, excluding the fifth, sixth and seventh
Hu-moments and eccentricity. Recognition accuracy was 78.58% compared to 78.14%.
MNIST. Who is the best in MNIST? Electronic resource] - Access mode.— URL:https://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html /( date of appeal 18.10.2019)
Raspoznavanie rukopisnyih tsifr s ispolzovaniem svertochnyih neyronnyih setey v Python s Keras [Electronic resource] - Access mode. – URL:
Gonsales R., Vuds R., Eddins S. Tsifrovaya obrabotka izobrazheniy v srede MatLab. M: Tehnosfera, 2006. – 616 s.