Summary of Handwriting Recognition using Machine Learning
This project involved implementing machine learning algorithms including convolution
neural network (CNN), SVM, K-NN, and RF in classifying handwriting. The dataset used in the
study is obtained from the MINST dataset. The MNIST dataset is the binary form of images
constructed from NIST’s. This study was conducted in order to evaluate and compare the
performance of the three machine algorithms in recognizing blurry images. A concept that is useful
in various fields including medicine and other physical sciences. The algorithms were
implemented in the Jupiter notebook. The first process of implementation involved data
processing including cross-validation of K-Fold in grouping the data. The dataset was split into
training and validation datasets.
The MNIST dataset used in the current study was made of 10000 test graphics of bitmap
28 by 28 grayscale format. Prior to implementing the algorithm, missing data was inserted. The
size of the batch used to evaluate 10 differentiated epoch validation of accuracy and training was
7500. To the hidden layers, epoch 1 had a minimum accuracy of 16.48% for training and 16.47%
for validation. Epoch 1 equally established maximal accuracy of 93.38% and validation of 92.87%.
The results revealed that compared to SVM and RF, CNN and K-NN offer better
performance in handwriting recognition. The accuracy of CNN and K-NN were both reported to
be greater than 99%. The accuracy of CNN was recorded as 99.44%. Pre-processing was observed
to increase the accuracy of the machine learning algorithm. The findings of the current study
revealed that machine learning algorithms are applicable in handwriting recognition, a process that
involved training a model and applying the trained model in the recognition process.