Table of contents:

1. Introduction
2. About the Dataset
3. Setting up Monk and Prerequisites
4. Downloading Dataset
5. Creating Projects and Experiments
6. Approach
7. Selecting the best model
8. Conclusion

Introduction:

It is an easy task for us to classify handwritten information but to computers, it is a disconcerting and daunting job. Handwritten character classification in general is a challenging task because there are innumerable ways in which any character can be written.

Neural networks have although redefined this task and also play a huge role in developing such classifiers, provided they are fed with a huge amount of data for it to be trained.

In this particular blog, we will explore the Russian alphabets and classify them in their handwritten form.

About The Dataset:

In the data set, we have got 14190 colored images distributed among 3 image folders, consisting of all 33 categories of Russian alphabets.

The first folder has striped backgrounds (with few horizontal and/or vertical lines), the second folder has white backgrounds and the third one has graph type backgrounds (with many ordered horizontal and vertical lines).

#handwritten-classifier #russian-letters #monk #classification #russian-dataset #ai