TITLE: KOHTD: Kazakh Offline Handwritten Text Dataset 

ABSTRACT: 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. The findings demonstrate KOHTD's 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.
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. The findings demonstrate KOHTD's 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.
KOHTD: Kazakh Offline Handwritten Text Dataset
http://arxiv.org/abs/2110.04075v1