HANDWRITTEN OPTICAL CHARACTER RECOGNITION: IMPLEMENTATION FOR KAZAKH LANGUAGE

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Date

2021

Journal Title

Journal ISSN

Volume Title

Publisher

СДУ хабаршысы - 2021

Abstract

Abstract. Many documents, including as invoices, taxes, memoranda, and surveys, historical data, and test replies, still require handwriting with the transformation to digital information interchange. Handwritten text recognition (HTR), which is an automatic approach to decode records using a computer, is required in this aspect. For this proposal, I present a study of the implementation of optical recognition algorithms for handwritten text in the Kazakh language, using a recently collected database. The database, called the Kazakh Autonomous Handwritten Text Dataset (KOHTD), contains more than 140,335 segmented images of handwritten exam papers. As an algorithm, I used the proposed model by Harald Scheidl, which consists of several layers of neural networks and an CTC decoder. The trained model by putting an interval of Ir = 0.01 and a batch size of 60 showed effective results with indicators of about 85% accuracy.

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Keywords

handwritten text recognition, KOHTD, neural networks, CNN, СДУ хабаршысы - 2021, №4

Citation

M. Kalken / HANDWRITTEN OPTICAL CHARACTER RECOGNITION: IMPLEMENTATION FOR KAZAKH LANGUAGE / СДУ хабаршысы - 2021