KOHTD: Kazakh offline handwritten text dataset

Toiganbayeva N. Kasem M. Abdimanap G. Bostanbekov K. Abdallah A. Alimova A. Nurseitov D.
October 2022 Elsevier B.V.

Signal Processing: Image Communication
2022 #108

Despite the transition to digital information exchange, many documents, such as invoices, taxes, memos and questionnaires, historical data, and answers to exam questions, still require handwritten inputs. In this regard, there is a need to implement Handwritten Text Recognition (HTR) which is an automatic way to decrypt records using a computer. Handwriting recognition is challenging because of the virtually infinite number of ways a person can write the same message. For this proposal we introduce Kazakh handwritten text recognition research, a comprehensive dataset of Kazakh handwritten texts is necessary. This is particularly true given the lack of a dataset for handwritten Kazakh text. In this paper, we proposed our extensive Kazakh offline Handwritten Text dataset (KOHTD), which has 3000 handwritten exam papers and more than 140335 segmented images and there are approximately 922010 symbols. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning. We used a variety of popular text recognition methods for word and line recognition in our studies, including CTC-based and attention-based methods. In this paper, we have implemented state-of-the-art deep learning-based methods for Handwriting recognition for KOHTD dataset to create several strong baselines. The findings demonstrate KOHTDs diversity. Also, we proposed a Genetic Algorithm (GA) for line and word segmentation based on random enumeration of a parameter. The dataset and GA code are available at https://github.com/abdoelsayed2016/KOHTD.

Benchmark dataset , Convolutional neural networks , Deep learning , Document analysis and recognition , Genetic algorithm , Handwritten Kazakh text recognition in Cyrillic

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Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
KazMunayGas Engineering LLP, Nur-Sultan, 010000, Kazakhstan
Satbayev University, Almaty, 050013, Kazakhstan
Information Technology Department, Assiut University, Assiut, 71515, Assiut, Egypt

Al-Farabi Kazakh National University
KazMunayGas Engineering LLP
Satbayev University
Information Technology Department

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