Volume 3 (1), June 2020, Pages 75-93

Pakpoom Mookdarsanit, Lawankorn Mookdarsanit

Chandrakasem Rajabhat University, Bangkok, Thailand, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.


Thai is a non-tonal language usage for 70 million speakers in Thailand. A variety of Thai handwrit-ten styles has been a challenge in handwriting recognition. In this paper, we propose a novel “ThaiWrittenNet” based on Convolutional Neural Network (ConvNet or CNN) with a cutout to identify the handwritten recognitions. Deep Belief Network (DBN) is also combined with Con-vNet to reduce network complexity. From the results, ThaiWrittenNet outperforms the flat Con-vNet and other handcrafted features with traditional machine learning algorithms. It appears that DBN helps ConvNet to improve the accuracy of Thai-handwritten recognition.


Handwriting recognition, Convolutional neural network, Deep belief network, Thai handwriting recognition





Ager, S. (2020). How many languages are there in the world?. Retrieved from:

Alom, Md. Z., Sidkike, P., Taha, T. M. & Asari, V. K. (2017). Handwritten Bangla digit recognition using deep learning, arXiv:1705.02680.

Alom, Md. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Esesn, B. C. V., Awwal, A. A. S. & Asari, V. K. (2018). The history began from AlexNet: A comprehensive survey on deep learning approaches. arXiv:1803.01164.

Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European conference on computer vision (pp. 404-417). Springer, Berlin, Heidelberg.

Boonkwan, P., & Supnithi, T. (2017, June). Bidirectional deep learning of context representation for joint word segmentation and POS tagging. In International Conference on Computer Science, Applied Mathematics and Applications (pp. 184-196). Springer, Cham.

Chaiwatanaphan, S., Pluempitiwiriyawej, C., & Wangsiripitak, S. (2017). Printed Thai character recognition using shape classification in video sequence along a line. Engineering Journal, 21(6), 37-45.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.

Daengsi, T., & Wuttidittachotti, P. (2019). QoE Modeling for Voice over IP: Simplified E-model Enhancement Utilizing the Subjective MOS Prediction Model: A Case of G. 729 and Thai Users. Journal of Network and Systems Management, 27(4), 837-859.

Dalal, N., & Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (Vol. 1, pp. 886-893). IEEE.

Emsawas, T., & Kijsirikul, B. (2016, August). Thai printed character recognition using long short-term memory and vertical component shifting. In Pacific Rim International Conference on Artificial Intelligence (pp. 106-115). Springer, Cham.

Fukushima, K. (1980). Biological cybernetics neocognitron: a self‐organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern36, 193-202.

Haruechaiyasak, C., Kongthon, A., Palingoon, P., & Trakultaweekoon, K. (2013, October). S-Sense: A sentiment analysis framework for social media sensing. In Proceedings of the IJCNLP 2013 Workshop on Natural Language Processing for Social Media (SocialNLP) (pp. 6-13).

Haruechaiyasak, C., Kongyoung, S., & Dailey, M. (2008, May). A comparative study on Thai word segmentation approaches. In 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (Vol. 1, pp. 125-128). IEEE.

Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.

Inthajakra, L., Prachyapruit, A., & Chantavanich, S. (2016). The Emergence of Communication Intellectual History in Sukhothai and Ayutthaya Kingdom of Thailand. Social Science Asia, 2(4), 32-41.

Ismayilov, E. & Mammadov, R. (2019). Parallel solution of features subset selection process for hand-printed character recognition. Azerbaijan Journal of High Performance Computing, 2(2), 170-177.

Klahan, A., Pannoi, S., Uewichitrapochana, P., & Wiangsripanawan, R. (2018, July). Thai Word Safe Segmentation with Bounding Extension for Data Indexing in Search Engine. In International Conference on Computing and Information Technology (pp. 83-92). Springer, Cham.

Koanantakool, H. T., Karoonboonyanan, T., & Wutiwiwatchai, C. (2009). Computers and the thai language. IEEE Annals of the History of Computing, 31(1), 46-61.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

Lapjaturapit, T., Viriyayudhakom, K., & Theeramunkong, T. (2018, May). Multi-candidate word segmentation using bi-directional LSTM neural networks. In 2018 International Conference on Embedded Systems and Intelligent Technology & International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES) (pp. 1-6). IEEE.

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

Liu, D., Bober, M. & Kittlet, J. (2019). Visual semantic information pursuit: a survey. arXiv:1903.05434

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep learning for generic object detection: A survey. International journal of computer vision128(2), 261-318.

Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.

Lyons, S. (2016, August). Quality of Thai to English Machine Translation. In Pacific Rim Knowledge Acquisition Workshop (pp. 261-270). Springer, Cham.

Mookdarsanit, L. & Mookdarsanit, P. (2019a). SiamFishNet: The deep investigation of Siamese fighting fishes. International Journal of Applied Computer Technology and Information Systems, 8(2), 40-46.

Mookdarsanit, L. & Mookdarsanit, P. (2019b). Thai herb recognition with medicinal properties using convolutional neural network. Suan Sunandha Science and Technology Journal, 6(2), 34-40.

Mookdarsanit, L. & Mookdarsanit, P. (2020a). An adversarial perturbation technique against reCaptcha image attacks. Journal of Science and Technology Buriram Rajabhat University (on print).

Mookdarsanit, L. & Mookdarsanit, P. (2020b). The insights in computer literacy toward HR intelligence: Some associative patterns between IT subjects and job positions. Journal of Science and Technology RMUTSB (on print).

Mookdarsanit, L. (2020). The intelligent genuine validation beyond online Buddhist amulet market. International Journal of Applied Computer Technology and Information Systems, 9(2), 7-11.

Mookdarsanit, P. & Mookdarsanit, L. (2018a). A content-based image retrieval of Muay-Thai folklores by salient region matching. International Journal of Applied Computer Technology and Information Systems, 7(2), 21-26.

Mookdarsanit, P. & Mookdarsanit, L. (2020). The autonomous nutrient and calorie analytics from a Thai food image. Journal of Faculty Home Economics Technology RMUTP (on print).

Mookdarsanit, P. & Rattanasiriwongwut, M. (2017b). Location Estimation of a Photo: A Geo-signature MapReduce Workflow. Engineering Journal, 21(3), 295-308.

Mookdarsanit, P. & Rattanasiriwongwut, M. (2017c). MONTEAN Framework: a magnificent outstanding native-Thai and ecclesiastical art network. International Journal of Applied Computer Technology and Information Systems, 6(2), 17-22.

Mookdarsanit, P. (2019). TGF-GRU: A Cyber-bullying Autonomous Detector of Lexical Thai across Social Media. NKRAFA JOURNAL OF SCIENCE AND TECHNOLOGY, 15, 50-58.

Mookdarsanit, P., & Gertphol, S. (2013, January). Light-weight operation of a failover system for Cloud computing. In 2013 5th International Conference on Knowledge and Smart Technology (KST) (pp. 42-46). IEEE.

Mookdarsanit, P., & Ketcham, M. (2016, February). Image Location Estimation of well-known Places from Multi-source based Information. In The 11th International Symposium on Natural Language Processing (p. 75).

Mookdarsanit, P., & Mookdarsanit, L. (2018). Contextual Image Classification towards Metadata Annotation of Thai-tourist Attractions. ITMSoc Transactions on Information Technology Management, 3(1), 32-40.

Mookdarsanit, P., & Mookdarsanit, L. (2018). Name and recipe estimation of thai-desserts beyond image tagging. Kasem Bundit Engineering Journal, 8, 193-203.

Mookdarsanit, P., & Mookdarsanit, L. (2018b). An Automatic Image Tagging of Thai Dance’s Gestures. In Joint Conference on ACTIS & NCOBA, Ayutthaya, Thailand (pp. 76-80).

Mookdarsanit, P., & Rattanasiriwongwut, M. (2017, January). GPS Determination of Thai-temple Arts from a Single Photo. In The 11th International Conference on on Applied Computer Technology and Information Systems, Bangkok, Thailand (pp. 42-47).

Mookdarsanit, P., Soimart, L., Ketcham, M., & Hnoohom, N. (2015, November). Detecting image forgery using XOR and determinant of pixels for image forensics. In 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 613-616). IEEE.

Nomponkrang, T., & Sanrach, C. (2016). The Comparison of Algorithms for Thai-Sentence Classification. International Journal of Information and Education Technology, 6(10), 801-808.

Olaode, A. & Naghdy, G. (2020). Adaptive bag-of-visual word modelling using stacked-autoencoder and particle swarm optimisation for the unsupervised categorisation of images. IET Image Processing. doi:10.1049/iet-ipr.2019.1160 

Olaode, A., Naghdy, G. & Todd, C. (2014). Unsupervised classification of images: A review. International Journal of Image Processing, 8(5), 325-342.

Pornpanomchai, C., Wongsawangtham, V., Jeungudomporn, S. & Chatsumpun, N. (2011). Thai handwritten character recognition by genetic algorithm. IACSIT International Journal of Engineering and Technology, 3(2), 148-153.

Raghu, M. & Schmidt, E. (2020). A survey of deep learning for scientific discovery, arXiv:2003.11755.

Rathi, R., Pandey, R. K., Chaturvedi, V. & Jangid, M. (2012). Offline handwritten Devanagari vowels recognition using KNN classifier. International Journal of Computer Applications, 49(23), 11-16.

Rumelhart, D. E. & McClelland, J. L. (1987). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations. (pp. 318-362).

Satienkoses, Y. (1981). Essays on Thai folklore. Duang Kamol, Bangkok.

Soimart, L., & Ketcham, M. (2015, January). The segmentation of satellite image using transport mean-shift algorithm. In 13th International Conference on IT Applications and Management (ITAM-13) (pp. 124-128).

Soimart, L. & Ketcham, M. (2016a). An efficient algorithm for earth surface interpretation from satellite imagery. Engineering Journal, 20(5), 215-228.

Soimart, L., & Ketcham, M. (2016, February). Hybrid of pixel-based and region-based segmentation for geology exploration from multi-spectral remote sensing. In The 11th International Symposium on Natural Language Processing (p. 74).

Soimart, L. & Mookdarsanit, P. (2016a). Gender estimation of a portrait: Asian facial-significance framework. In Proceedings of the 6th International Conference on Sciences and Social Sciences. Mahasarakham, Thailand.

Soimart, L., & Mookdarsanit, P. (2016). Multi-factor authentication protocol for information accessibility in flash drive. The 9th Applied Computer Technology and Information Systems, Nakhon Pathom, 10-13.

Soimart, L. & Mookdarsanit, P. (2017a). Ingredients estimation and recommendation of Thai-foods. SNRU Journal of Science and Technology, 9(2), 509-520.

Soimart, L. & Mookdarsanit, P. (2017b). Name with GPS auto-tagging of Thai-tourist attractions from an image. In Proceedings of the 2nd Technology Innovation Management and Engineering Science International Conference (pp. 211-217). Nakhon Pathom, Thailand.

Soimart, L. & Pongcharoen, P. (2011). Multi-row machine layout design using aritificial bee colony. In Proceedings of 2011 International Proceedings of Economics Development & Research. Bangkok, Thailand: IPEDR

Sornlertlamvanich, V., Potipiti, P, Wutiwiwatchai, C. & Mittrapiyanuruk, P. (2000). The state of the art in Thai language processing. In Proceedings of the 38th Annual Meeting on Association for Computational Linguistics (pp. 1-2). Stroudsburg, PA, USA: ACM.

Srihari, S. N. & Kuebert, E. J. (1997). Integration of hand-written address interpretation technology into the United States Postal Service Remote Computer Reader system. In Proceedings of the 4th International Conference on Document Analysis and Recognition (pp. 892-896). ACM.

Surinta, O. & Nitsuwat, S. (2006). Handwritten Thai character recognition using Fourier descriptors and robust C-prototype. Information Technology Journal. 2(1), 92-96

Surinta, O., Karaaba, M. F., Schomaker, L. R. B. & Wiering, M. A. (2015). Recognition of handwritten characters using local gradient feature descriptors. Engineering Applications of Artificial Intelligence, 45, 495-414.

Theeramunkong, T., & Tanhermhong, T. (2004). Pattern-based features vs. statistical-based features in decision trees for word segmentation. IEICE TRANSACTIONS on Information and Systems, 87(5), 1254-1260.

Torfi, A., Shivani, R. A., Keneshloo, Y., Tavvaf, N. & Fox, E. A. (2020). Natural language processing advancements by deep learning: A survey, arXiv:2003.01200.

Wat Chonprathan Rangsarit. (2001). A Buddhism preachment by Phra Phrom Mangkhalachan. Department of Science Service Journal. 49(155), 33-34. (in Thai).

World Bank. (2018). Population, Total. Retrieved from:

Zheng, L., Yang, Y. & Tian, Q. (2017). SIFT meets CNN: A decade survey of instance retrieval. arXiv:1608.01807.

Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: A survey. arXiv:1905.05055.