scholarly journals Utilization of the Cube as a Medium for the Introduction of the English Alphabet for Preschoolers

2021 ◽  
Vol 1908 (1) ◽  
pp. 012033
Author(s):  
Hendra Pradibta ◽  
Usman Nurhasan ◽  
Eka Pratitis ◽  
Khusnul Krisiananda
Keyword(s):  
Why English? ◽  
2016 ◽  
pp. 142-153
Author(s):  
Pauline Bunce
Keyword(s):  

Author(s):  
Gerjan van Schaaik

This chapter presents the Latin-based alphabet of Turkish, which differs from that of English in the extra letters ç, ğ, ı, ö, ş, and ü, whereas it lacks q, w, and x. A detailed account is given of vowels, of consonants not present in the English alphabet, and of consonants shared by both languages. The notions front and back for vowels are introduced, as well as the notions voiced versus voiceless for consonants. Next, attention is given to aspiration of voiceless plosives. The most conspicuous letters for which the phonological environment determines their sound value are r and ğ; the former being pronounced with a kind of rustling at the end of a word, and the latter functioning either as a lengthening marker or as a symbol representing the y-sound. This chapter ends with the Turkish telephone alphabet.


1994 ◽  
Vol 78 (1) ◽  
pp. 83-88 ◽  
Author(s):  
Shoji Itakura ◽  
Hiroshi Imamizu

30 children (18 boys and 12 girls) with a mean age of 4 yr., 8 mo. were subjects in an experiment testing the relative dominance of visual and tactual modalities in mirror-image shape discrimination. The sets of unfamiliar stimuli (written and wooden letters of the English alphabet, P, B, C, U, R, F) were presented to the children randomly. Children matched the stimulus with either another visual or another tactual shape. Analysis suggests touch is not inferior to vision in mirror-image shape discrimination. These results are different from those of previous reports comparing tactual and visual discrimination with nonmirror-image patterns.


Author(s):  
Gulfeshan Parween

Abstract: In this paper, we present a scheme to develop to complete OCR system for printed text English Alphabet of Uppercase of different font and of different sizes so that we can use this system in Banking, Corporate, Legal industry and so on. OCR system consists of different modules like preprocessing, segmentation, feature extraction and recognition. In preprocessing step it is expected to include image gray level conversion, binary conversion etc. After finding out the feature of the segmented characters artificial neural network and can be used for Character Recognition purpose. Efforts have been made to improve the performance of character recognition using artificial neural network techniques. The proposed OCR system is capable of accepting printed document images from a file and implemented using MATLAB R2014a version. Key words: OCR, Printed text, Barcode recognition


2020 ◽  
Vol 176 (14) ◽  
pp. 31-37
Author(s):  
Aktar Hossen ◽  
Feng Xiufang ◽  
Xiong XiaoQiao ◽  
Zhang Xin

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