การพัฒนาขั้นตอนวิธีและนำเสนอแนวทางการใช้งานการตรวจสอบการเขียนภาษาไทยด้วยการประมวลผลภาพ สำหรับนักเรียนชั้นประถมศึกษาตอนปลาย กรณีศึกษาโรงเรียนบ้านสันป่าไร่
Year: 2026
Type: article
Abstract: This research aims to 1) Apply Zipf’s Law in the analysis of Thai vocabulary, 2) Apply language patterns in word classification to examine Thai writing, 3) Develop a system for checking Thai sentence writing, and 4) Propose guidelines for using image processing-based Thai writing verification through quantitative research. The studied data consisted of Thai writing samples from 37 upper-elementary students (late primary level) at Ban San Parai School, Tak Province. Research instruments included a Thai vocabulary test, a writing test, an image-processing-based writing verification system, descriptive statistics, model performance testing, and expert validation. The research findings were as follows: 1) The application of Zipf’s Law in analyzing Thai vocabulary knowledge enabled the classification of words into 4 groups based on quartiles: most frequently written words, easy words, medium words, and difficult words. 2) The application of a Trigram language model combined with handwriting recognition and Convolutional Neural Network was able to classify words from handwritten images and correctly reorder them to match the original sentences with 100% accuracy, even when the input data had uncertain ordering. 3) The developed algorithm for checking Thai sentence writing achieved an Accuracy, Precision, Recall, and F1-score = 1.0 on both training and test sets. There were no errors in the Confusion Matrix for both Sentence 1 and Sentence 2. 4) The proposed guidelines for using image-processing-based Thai writing verification were deemed highly appropriate, with an average suitability score of 4.40 (on a 5-point scale), corresponding to a “high” level of appropriateness.
Source: วารสารวิชาการบัณฑิตศึกษา
Institution: Nakhon Sawan Rajabhat University
Language: Thai
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7220293198