KAZAKH HANDWRITING RECOGNITION

Recognition of handwritten text is one aspect of objectrecognition and known as handwriting detection cause of a computer’spotential to recognize and comprehend readable handwriting from resourcesincluding paper files, touch smart devices, images, etc. Data is categorized intoa number of classes or groups using pattern recognition. The paper presents asuccessful experiment in recognizing handwritten Kazakh text usingConvolutional Recurrent Neural Network based architectures and the KazakhAutonomous Handwritten Text Dataset. The proposed algorithm achieved anoverall accuracy of 86.36% and showed promising results. However, the papersuggests that further research could be conducted to improve the model, suchas correlating and enlarging the database or incorporating other models andlibraries. Additionally, the paper emphasizes the importance of consideringlanguage specifics when building a text recognition model, as modernalgorithms that work well in one language may not guarantee the sameperformance in another.