Dataset Information

The classification of handwriting features of the Kazakh language written in Latin script.

ABSTRACT: In 2017, the Republic of Kazakhstan began the phased transition of its alphabet from Cyrillic to Latin script. This transition has presented significant challenges to Kazakhstani document examiners, who have yet to develop appropriate methodologies for the analysis of handwriting samples written in the Kazakh language using Latin letters. This study aims to identify distinguishing macro and micro features of letters within Kazakh writing samples produced using the Latin alphabet and determine their frequencies of occurrence and discriminating power indices. Micro features were examined using the four most frequently appearing letters: "a", "y", "e" and "n". A comparative analysis of tested Latin letters with those of a similar configuration in Cyrillic demonstrated differences in the number of distinguishing features, as well as in the frequency of occurrence and discriminating power indices of similar features. These results show that separate statistical bases should be used for Latin and Cyrillic letters when analysing handwriting samples based on the frequencies of occurrence of micro and macro writing features.

SUBMITTER: Galymzhanova A

PROVIDER: S-EPMC9930772 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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The classification of handwriting features of the Kazakh language written in Latin script.

Galymzhanova Akyldana A Gooch James J Frascione Nunzianda N

Forensic sciences research 20220413 4

In 2017, the Republic of Kazakhstan began the phased transition of its alphabet from Cyrillic to Latin script. This transition has presented significant challenges to Kazakhstani document examiners, who have yet to develop appropriate methodologies for the analysis of handwriting samples written in the Kazakh language using Latin letters. This study aims to identify distinguishing macro and micro features of letters within Kazakh writing samples produced using the Latin alphabet and determine th ...[more]

PMID: 36817231

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Project description:Do handwriting skills transfer when a child writes in two different scripts, such as the Latin and Cyrillic alphabets? Are our measures of handwriting skills intrinsically bound to one alphabet or will a child who faces handwriting difficulties in one script experience similar difficulties in the other script? To answer these questions, 190 children from grades 1-4 were asked to copy a short text using both the Cyrillic and Latin alphabets on a digital tablet. A recent change of policy in Kazakhstan gave us an opportunity to measure transfer, as the Latin-based Kazakh alphabet has not yet been introduced. Therefore, pupils in grade 1 had a 6-months experience in Cyrillic, and pupils in grades 2, 3, and 4 had 1.5, 2.5, and 3.5 years of experience in Cyrillic, respectively. This unique situation created a quasi-experimental situation that allowed us to measure the influence of the number of years spent practicing Cyrillic on the quality of handwriting in the Latin alphabet. The results showed that some of the differences between the two scripts were constant across all grades. These differences thus reflect the intrinsic differences in the handwriting dynamics between the two alphabets. For instance, several features related to the pen pressure on the tablet are quite different. Other features, however, revealed decreasing differences between the two scripts across grades. While we found that the quality of Cyrillic writing increased from grades 1-4, due to increased practice, we also found that the quality of the Latin writing increased as well, despite the fact that all of the pupils had the same absence of experience in writing in Latin. We can therefore interpret this improvement in Latin script as an indicator of the transfer of fine motor control skills from Cyrillic to Latin. This result is especially surprising given that one could instead hypothesize a negative transfer, i.e., that the finger controls automated for one alphabet would interfere with those required by the other alphabet. One interesting side-effect of these findings is that the algorithms that we developed for the diagnosis of handwriting difficulties among French-speaking children could be relevant for other alphabets, paving the way for the creation of a cross-lingual model for the detection of handwriting difficulties.

Project description:The development of the machine translation field was driven by people's need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages by artificially increasing the size of the corpora and improving the performance of the Kazakh-English machine translation models. These methods are called forward translation, backward translation, and transfer learning. Then the Sequence-to-Sequence (recurrent neural network and bidirectional recurrent neural network) and Transformer neural machine translation architectures with their features and specifications are concerned for conducting experiments in training models on parallel corpora. The experimental part focuses on building translation models for the high-quality translation of formal social, political, and scientific texts with the synthetic parallel sentences from existing monolingual data in the Kazakh language using the forward translation approach and combining them with the parallel corpora parsed from the official government websites. The total corpora of 380,000 parallel Kazakh-English sentences are trained on the recurrent neural network, bidirectional recurrent neural network, and Transformer models of the OpenNMT framework. The quality of the trained model is evaluated with the BLEU, WER, and TER metrics. Moreover, the sample translations were also analyzed. The RNN and BRNN models showed a more precise translation than the Transformer model. The Byte-Pair Encoding tokenization technique showed better metrics scores and translation than the word tokenization technique. The Bidirectional recurrent neural network with the Byte-Pair Encoding technique showed the best performance with 0.49 BLEU, 0.51 WER, and 0.45 TER.

Dataset Information

The classification of handwriting features of the Kazakh language written in Latin script.

Publications

The classification of handwriting features of the Kazakh language written in Latin script.

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