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Diacritic-aware Yorùbá spell checker

Asahiah Franklin Oládiípò, Onífádé Mary Taiwo, Asahiah Adekemisola Olufunmilayo, Adegunlehin Abayomi Emmanuel, Amoo Adekemi Olawunmi

Computer Science

2023

T. 24 (1)

31--51

Spell checking and correction is still in its infancy for the Yorùbá language; existing tools cannot be directly applied to address the problem, as Yorùbá uses diacritics extensively for distinguishing phonemes and for marking tone. A model was formulated as a parallel combination of a unigram language model and a diacritic model to form a dictionary sub-model that can be used by error-detection and candidate-generation modules. The candidate-generation module was implemented as a reverse Levensthein edit-distance algorithm. The system was evaluated by using detection accuracy (calculated from the precision and recall) and suggestion accuracy (SA) as metrics. Our experimental setups compared the performance of the component subsystems when used alone and with their combination into a unified model. The detection accuracies for the different models range from 93.23 to 95.01%, and the suggestion accuracies range from 26.94 to 72.10%. The results indicated that each of the sub-models in the dictionary played different roles.

Recognition of handwritten Latin characters with diacritics using CNN

Lukasik Edyta, Charytanowicz Małgorzata, Milosz Marek, Tokovarov Michail, Kaczorowska Monika, Czerwinski Dariusz, Zientarski Tomasz

Bulletin of the Polish Academy of Sciences. Technical Sciences

2021

Vol. 69, nr 1

art. no. e136210

Convolutional Neural Networks (CNN) have achieved huge popularity in solving problems in image analysis and in text recognition. In this work, we assess the effectiveness of CNN-based architectures where a network is trained in recognizing handwritten characters based on Latin script. European languages such as Dutch, French, German, etc., use different variants of the Latin script, so in the conducted research, the Latin alphabet was extended by certain characters with diacritics used in Polish language. To evaluate the recognition results under the same conditions, a handwritten Latin dataset was also developed. The proposed CNN architecture produced an accuracy of 96% for the extended character set. This is comparable to state-of-the-art results found in the domain of identifying handwritten characters. The presented approach extends the usage of CNN-based recognition to different variants of the Latin characters and shows it can be successfully used for a set of languages based on that script. It seems to be an effective technique for a set of languages written using the Latin script.

Digitalizacja karakteru ukośnego Jana Januszowskiego - studium procesu

Wieluńska A.

Acta Poligraphica

2018

Vol. 12

31--50

The article concerns the revitalization of the sixteenth-century typeface and its digitalization into a computer font. Research on historical prints in which Nowy Karakter Polski occurs is presented. The paper describes main problems of design process related to the reconstruction of characters from historical prints, and the addition of new glyphs necessary in the character set of modern font. A significant part of the design work concerned decorative alternate letters.