MULTI-AGENT INTELLIGENT SIMULATION LABORATORY  (MISL)

Intelligent Autonomous Systems (IAS Group), Faculty of Informatics
Mahasarakham University, Thailand

Olarik Surinta

OLARIK SURINTA

Multi-agent Intelligent Simulation Laboratory (MISL)
Department of Information Technology, Faculty of Informatics
Mahasarakham University, THAILAND
email: olarik.s@msu.ac.th
cv: view detail 
profile: scopuus, google scholar
ORCiD: 0000-0002-0644-1435

 Olarik Surinta grew up in Chiang Mai, Thailand and received his BBA from Rajamangala Institute of Technology and his MSc from King Mongkut’s Institute of Technology North Bangkok. He started his career in 2004 as a lecturer at the department of information technology in the faculty of informatics, Mahasarakham University. Since 2011, he has been promoted to assistant professor. In 2016, he graduated Ph.D. at University of Groningen, Institute of Artificial Intelligence and Cognitive Engineering (ALICE) under the supervision of Prof. dr. Lambert Schomaker and Dr. Marco Wiering

Education


PhD, 2016 , Artificial Intelligence and Cognitive Engineering, University of Groningen

MSc, 2003, Information Technology, King Mongkut’s Institute of Technology North Bangkok

BSc, 1999, Information Systems, Rajamangala Institute of Technology

Research Interests


  • Artificial intelligence
  • Machine learning
  • Handwritten recognition
    • Document layout analysis
    • Handwritten word/character recognition
    • Word spotting
  • Computer vision
    • Face recognition

Dissertation: Multi-Script Handwritten Character Recognition using Feature Descriptors and Machine Learning

There exists no generic method for recognizing handwritten scripts from different writing systems, cultures or historical periods. Asian scripts pose a number of interesting fundamental problems at the levels of image processing, text segmentation, feature extraction, shape classification and language modeling. Instead of spending human efforts at each of these level, the current challenge is to exploit machine learning methods. The main objective of the project is to automatically recognize handwritten Thai and to automatically convert documents written in Thai to text files.

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Featured Publications

Master Thesis

Check Out Our Work

A Machine Learning Based Approach for Detecting Distributed Denial of Service Attacks

A Machine Learning Based Approach for Detecting Distributed Denial of Service Attacks การเรียนรู้ของเครื่องจักรเพื่อการตรวจจับการโจมตีโดยปฏิเสธการให้บริการแบบกระจาย

Methods of Silk Pattern Image Retrieval with Small Sample Sizes

Methods of Silk Pattern Image Retrieval with Small Sample Sizes กระบวนการเพื่อการค้นคืนรูปภาพลายผ้าไหมที่มีกลุ่มตัวอย่างน้อย

Applying deep learning techniques for plant recognition in natural environment

applying deep learning techniques for plant recognition in natural environment การประยุกต์ใช้เทคนิคการเรียนรู้เชิงลึกสำหรับการรู้จำพรรรไม้ที่อยู่ในสิ่งแวดล้อมทางธรรมชาติ