Research Article
Chauvinism in Ghanaian Paremiology and Communication: A Reconstructive Review of Ewe, Twi, Fante, and Kasena Proverbial Expressions
Issue:
Volume 2, Issue 2, June 2026
Pages:
78-90
Received:
20 December 2025
Accepted:
8 January 2026
Published:
30 May 2026
Abstract: Every culture has its own unique proverbs or proverbial expressions that express perceived truths based on common sense or the daily and historical experiences of its folks. Although the wisdom that traditional sayings convey is not necessarily an entire representation of the cultural values of a specific culture, they do, to a large extent, reflect the values upheld by those cultures. If a true link between cultural values and proverbs is acknowledged, old proverbs might illustrate past values, while a new repertoire of proverbs would probably reflect the morals and values of modern times. Over the years, Ghanaians have employed proverbs to keep women in perpetual subjugation and subservience to men, relegating them to the background and paying little attention to the enormous contribution of women to the development of society. Has this cultural portrait changed over time? How are these denigrating portrayals effected? Can anything be done to reverse the narrative? In answering these questions adequately, the study would present and analyse Ghanaian proverbs that embody chauvinist sentiments, sourced from across a number of languages: Ewe, Twi, Fante, and Kasena. The analysis of these sayings allows for an assessment, discussion, deconstruction, and reconstruction of the image of the female in the Ghanaian society of the 21st century.
Abstract: Every culture has its own unique proverbs or proverbial expressions that express perceived truths based on common sense or the daily and historical experiences of its folks. Although the wisdom that traditional sayings convey is not necessarily an entire representation of the cultural values of a specific culture, they do, to a large extent, reflec...
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Research Article
A Korean Handwritten Text Recognition Method Based on CSWin Transformer and GPT Language Model
Il-Nam Pak,
Myong-Chol Kim,
Hyon-Gwang O,
HakHo Hong*
Issue:
Volume 2, Issue 2, June 2026
Pages:
91-98
Received:
14 December 2025
Accepted:
15 January 2026
Published:
10 June 2026
Abstract: HTR is a challenging problem in the field of pattern recognition, as there are many variations in writing style, slope, size, noise, etc., depending on the individual writter’s habits, unlike PTR(Print Text Recognition). In this paper, we design a CSWin transformer-based encoder and a GPT-2 language model based decoder to improve the per-formance of Korean handwritten text recognition. We have improved CSWin transformer based on the self-attention in horizontal and vertical stripes to suit the characteristics of handwritten text recognition, used it as an encoder. The main features of CSWin transformer is the self-attention modules in the horizontal and vertical stripes and local enhancement positional encoding module. The local enhancement positional encoding module can support any image resolution, and is especially advantageous for HTR with line width varying frequently. In this paper, we use the local enhance positional encoding module. We also applied a projection transform to the encoder's features to transform to language-like tokens, and designed a decoder based on the Korean language model. we constructed a Korean subword vocabulary based on the byte pair encoding algorithm. The text corpus used for subword vocabulary construction contains 16,181,377 lines extracted from newspapers, common sense, novels, dialogues, and dictionaries. Since in Korean, too long sequence can be selected into a subword unlike English, so the maximum length of a subword is set to 5 to prevent the length of a subword from being too long when constructing a subword vocabulary. The final subword vocabulary contains 15477 subwords, and we use these subwords as the language tokens. Consequently, we have proposed a Korean handwritten text recognition method consisting of a CSWin transformer-based encoder and a GPT language model-based decoder. Experimental results show that our method is an efficient and effective method for Korean handwriting text recognition.
Abstract: HTR is a challenging problem in the field of pattern recognition, as there are many variations in writing style, slope, size, noise, etc., depending on the individual writter’s habits, unlike PTR(Print Text Recognition). In this paper, we design a CSWin transformer-based encoder and a GPT-2 language model based decoder to improve the per-formance o...
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