KAZAKH HANDWRITING RECOGNITION

Abstract

Recognition of handwritten text is one aspect of object
recognition and known as handwriting detection cause of a computer’s
potential to recognize and comprehend readable handwriting from resources
including paper files, touch smart devices, images, etc. Data is categorized into
a number of classes or groups using pattern recognition. The paper presents a
successful experiment in recognizing handwritten Kazakh text using
Convolutional Recurrent Neural Network based architectures and the Kazakh
Autonomous Handwritten Text Dataset. The proposed algorithm achieved an
overall accuracy of 86.36% and showed promising results. However, the paper
suggests that further research could be conducted to improve the model, such
as correlating and enlarging the database or incorporating other models and
libraries. Additionally, the paper emphasizes the importance of considering
language specifics when building a text recognition model, as modern
algorithms that work well in one language may not guarantee the same
performance in another.