Segmentation of On-Line Handwritten Japanese Text Using SVM for Improving Text Recognition

Zhu, Bilan; Tokuno, Junko; Nakagawa, Masaki

doi:10.1007/11669487_19

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, SVM is a widely used classifier in this type of research using machine learning algorithms, as it can accurately predict and tag POS tags [13]. Additionally, SVM predicts the POS tags for unknown words [14] [15] and is considered one of the most efficient and accurate classification techniques for POS tagging [16]. According to [14], this classifier is based on sub-words, contextual information, and environmental context tagger, and [16] noted that this classifier could be used to classify sentences, ambiguity classes, and word length.…”

Section: Related Workmentioning

confidence: 99%

Improved POS Tagging Model for Malay Twitter Data based on Machine Learning Algorithm

Ariffin¹,

Tiun²

2022

IJACSA

View full text Add to dashboard Cite

Twitter is a popular social media platform in Malaysia that allows for 280-character microblogging. Almost everything that happens in a single day is tweeted by users. Because of the popularity of Twitter, most Malaysians use it daily, providing researchers and developers with a wealth of data on Malaysian users. This paper explains why and how this study chose to create a new Malay Twitter corpus, Malay Part-of-Speech (POS) tags, and a Malay POS tagger model. The goal of this paper is to improve existing Malay POS tags so that they are more compatible with the newly created Malay Twitter corpus, as well as to build a POS tagging model specifically tailored for Malay Twitter data using various machine learning algorithms. For instance, Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) classifiers. This study's data was gathered by using Twitter's Advanced Search function and relevant and related keywords associated with informal Malay. The data was fed into machine learning algorithms after several stages of processing to serve as the training and testing corpus. The evaluation and analysis of the developed Malay POS tagger model show that the SVM classifier, as well as the newly proposed Malay POS tags, is the best machine learning algorithm for Malay Twitter data. Furthermore, the prediction accuracy and POS tagging results show that this research outperformed a comparable previous study, indicating that the Malay POS tagger model and its POS were successfully improved.

show abstract

Section: Related Workmentioning

confidence: 99%

Improved POS Tagging Model for Malay Twitter Data based on Machine Learning Algorithm

Ariffin¹,

Tiun²

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…After the quantization of line direction, we extract multi-dimensional features, such as distance and overlap between adjacent strokes, from each offstroke and apply the SVM to the extracted features to produce segmentation point candidates [51]. Character size may vary among text line elements, so we estimate the character size again for every text line element.…”

Section: Hypothetical Segmentation For Each Text Line Elementmentioning

confidence: 99%

Recent Results of Online Japanese Handwriting Recognition and Its Applications

Nakagawa

Tokuno

Zhu

et al.

Arabic and Chinese Handwriting Recognition

Self Cite

View full text Add to dashboard Cite

Abstract. This paper discusses online handwriting recognition of Japanese characters, a mixture of ideographic characters (Kanji) of Chinese origin, and the phonetic characters made from them. Most Kanji character patterns are composed of multiple subpatterns, called radicals, which are shared among many (sometimes hundreds of) Kanji character patterns. This is common in Oriental languages of Chinese origin, i.e., Chinese, Korean and Japanese. It is also common that each language has thousands of characters. Given these characteristics, structured character pattern representation (SCPR) composed of subpatterns is effective in terms of the size reduction of a prototype dictionary (a set of prototype patterns) and the robustness to deformation of common subpatterns. In this paper, we show a prototype learning algorithm and HMM-based recognition for SCPR. Then, we combine the SCPR-based online recognizer with a compact offline recognizer employing quadratic discriminant functions. Moreover, we also discuss online handwritten Japanese text recognition and propose character orientation-free and line direction-free handwritten text recognition and segmentation. Finally, as applications of online handwritten Japanese text recognition, we show segmentation of mixed objects of text, formulas, tables and line-drawings, and handwritten text search.

show abstract

Segmentation of On-Line Handwritten Japanese Text Using SVM for Improving Text Recognition

Cited by 2 publications

References 3 publications

Improved POS Tagging Model for Malay Twitter Data based on Machine Learning Algorithm

Improved POS Tagging Model for Malay Twitter Data based on Machine Learning Algorithm

Recent Results of Online Japanese Handwriting Recognition and Its Applications

Contact Info

Product

Resources

About