Olarik Surinta

Mahasarakham University · Department of Information Technology

This paper describes the use of a novel A* path-planning algorithm for performing line segmentation of handwritten documents. The novelty of the proposed approach lies in the use of a smart combination of simple soft cost functions that allows an artificial agent to compute paths separating the upper and lower text fields. The use of soft cost func...

In this paper we propose to use local gradient feature descriptors, namely the scale invariant feature transform keypoint descriptor and the histogram of oriented gradients, for handwritten character recognition. The local gradient feature descriptors are used to extract feature vectors from the handwritten images, which are then presented to a mac...

Predicting the sequence pattern of the handwritten text images is a challenging problem due to various writing styles, insufficient training data, and also background noise appearing in the text images. The architecture of the combination between convolutional neural network (CNN) and recurrent neural network (RNN), called CRNN architecture, is the...

The ensemble learning method is a necessary process that provides robustness and is more accurate than the single model. The snapshot ensemble convolutional neural network (CNN) has been successful and widely used in many domains, such as image classification, fault diagnosis, and plant image classification. The advantage of the snapshot ensemble C...

Many problems can reduce handwritten character recognition performance, such as image degradation, light conditions, low-resolution images, and even the quality of the capture devices. However, in this research, we have focused on the noise in the character images that could decrease the accuracy of handwritten character recognition. Many types of...

Presently, subtitles are embedded into videos and placed on their bottom line. Locating the subtitle area and recognizing the text in the image is not simple. In this paper, we propose using the fusion convolutional recurrent neural network (CRNN) to recognize multi-language (Thai and English) from the subtitle word images. We fused the state-of-th...

Currently, social networks, where people can express their opinion through content and comments, are fast developing and affect various areas of daily life; Particularly, some research on YouTube travel channels found that almost tourists and audiences leave comments about their attitudes to that place. Thus, mining the emotional recognition of com...

Stopping violent incidents in real life is more dangerous for ordinary people. It may harm people's lives. Calling the police is the best choice to stop the violence. We should have an automatic system to recognize violence and warn the police on time. This paper proposes a method to classify violent incidents from video. However, classification of...

The World Health Organization found that more than 34 million people suffer from hearing loss and these people need to use sign language to communicate. Hence, the sign language recognition system is proposed to communicate with hearing loss people and others. In this paper, we aim to propose an end-to-end system to recognize the dynamic Thai finge...

Rapid diagnosis increases the chance of a patient being cured of symptoms. This applies especially to diabetic diseases where there is a high risk of diabetic retinopathy, which will lead to blindness if not treated promptly. Artificial intelligent techniques are proposed to diagnose diabetic retinopathy. In this paper, we recognize diabetic retino...

Plant disease is the most common problem in agriculture. Usually, the symptoms appear on leaves of the plants which allow farmers to diagnose and prevent the disease from spreading to other areas. An accurate and consistent plant disease recognition system can help prevent the spread of diseases and save maintenance costs. In this research, we pres...

Nowadays, many videos are published on Internet channels such as Youtube and Facebook. Many audiences, however, cannot understand the contents of the video, maybe due to the different languages and even hearing impairment. As a result, subtitles have been added to videos. In this paper, we proposed deep learning techniques, which are the combinatio...

The high-grade quality of agricultural goods can be affected by diseases. Therefore, farmers need to quickly stop the spread of diseases. This study proposes a stacking ensemble of lightweight learning convolutional neural network (CNN) framework to enhance the recognition accuracy of plant leaf disease images. In the proposed framework , we first...

The WHO reports that approximately 34 million people worldwide experience deafness and hearing loss. In 2050, these will increase to affect 900 million people. It is essential to communicate with the hearing impaired in hand sign language. This paper proposes an end-to-end fingerspelling recognition framework of the Thai sign language based on deep...

In this paper, we have demonstrated the effectiveness of the fusion convo-lutional neural network (CNN) and ensemble CNN architectures for diabetic retinopathy classification. Due to the fusion and ensemble CNN architectures, we proposed to use five CNN architectures consisting of InceptionV3, ResNet50, ResNet50V2, Xception, and DenseNet121 to find...

Nowadays, many videos have been published on Internet channels such as Youtube and Facebook. Many audiences, however, cannot understand the contents of the video, maybe due to the different languages and even hearing impairment. As a result, subtitles have been added to videos. In this paper, we proposed deep learning techniques, which were the com...

Nowadays, many videos have been published on Internet channels such as Youtube and Facebook. Many audiences, however, cannot understand the contents of the video, maybe due to the different languages and even hearing impairment. As a result, subtitles have been added to videos. In this paper, we proposed deep learning techniques, which were the com...

Water resource management is one of the biggest challenges that are being faced, such as a warming climate, arid land, and toxic chemicals in the water. It is essential to deal with water resource management urgently. In this article, researchers mainly focus on monitoring the water quality in the Lam Pa Thao dam, Chaiyaphum, Thailand. The farmer i...

The approval of a bank's credit for an individual loan requires the fulfillment of several requirements, such as bank credit policy, loan amount, the purpose of the loan, and repayment ability. However, every type of credit is subject to the risk of non-repayment and non-performing loans, which affect the liquidity of the bank's operation. This res...

This research presents an application of the internet of things (IoT) technology. The technology is responsible for checking the temperature and humidity in a mushroom cultivation house and the operation of the IoT control box. It is a semi-automated system that does not rely on farmers' labor. The system can be checked and operated through an appl...

Use of ensemble convolutional neural networks (CNNs) has become a more robust strategy to improve image classification performance. However, the success of the ensemble method depends on appropriately selecting the optimal weighted parameters. This paper aims to automatically optimize the weighted parameters using the differential evolution (DE) al...

There is a global increase in health awareness. The awareness of changing eating habits and choosing foods wisely are key factors that make for a healthy life. In order to design a food image recognition system, many food images were captured from a mobile device but sometimes include non-food objects such as people, cutlery, and even food decorati...

Face verification systems have many challenges to address because human images are obtained in extensively variable conditions and in unconstrained environments. Problem occurs when capturing the human face in low light conditions, at low resolution, when occlusions are present, and even different orientations. This paper proposes a face verificati...

Plant diseases are one of the most serious issues that can decrease the value and volume of plant goods. It is time-consuming for farmers to discover and identify the disease by observing the leaves of plants, even with specialists scientists and laboratory processes. This study proposed the deep learning approach to address the real-world problems...

Recognition of plant leaves and diseases from images is a challenging task in computer vision and machine learning. This is because various problems directly affect the performance of the system, such as the leaf structure, differences of the intra-class, similarity of shape between inter-class, perspective of the image, and even recording time. In...

The analysis of land use and land cover is a task of remote sensing and geographic information systems. Nowadays, deep learning techniques can analyze land use and land cover with high performance. In this paper, we focus on the classification of land use for Thailand's economic crops based on the convolutional neural network (CNN) technique. We ev...

Vehicle Type Recognition has a significant problem that happens when people need to search for vehicle data from a video surveillance system at a time when a license plate does not appear in the image. This paper proposes to solve this problem with a deep learning technique called Convolutional Neural Network (CNN), which is one of the latest advan...

Vehicle Type Recognition has a significant problem that happens when people need to search for vehicle data from a video surveillance system at a time when a license plate does not appear in the image. This paper proposes to solve this problem with a deep learning technique called Convolutional Neural Network (CNN), which is one of the latest advan...

The identification process of plant species is one of the significant and challenging problems. In this research area, many researchers have focused on identifying the plant leaf images because the leaves of a plant are found almost all year round. The achieve method of the plant leaf image recognition is based on unique extraction features from th...

The real-world food image is a challenging problem for food image classification, because food images can be captured from different perspective and patterns. Also, many objects can appear in the image, not just foods. To recognize food images, in this paper, we propose a modified MobileNet architecture that is applies the global average pooling la...

For handwritten character recognition, a common problem is that each writer has unique handwriting for each character (e.g. stroke, head, loop, and curl). The similarities of handwritten characters in each language is also a problem. These similarities have led to recognition mistakes. This research compared deep Convolutional Neural Networks (CNNs...

Land use is constantly changing, and water plays a critical role in the process. If changes are noticed quickly or are predictable, land use planning and policies can be devised to mitigate almost any problem. Accordingly, researchers present a mask region-based convolutional neural network (Mask R-CNN) for water body segmentation from aerial image...

Thai silk fabrics have unique patterns in different regions of Thailand. The designers may have been inspired and took ideas from the natural environment to create new silk patterns. Hence, many new silk patterns are modified from the original silk pattern. It is challenging for people to recognize a pattern without any prior knowledge and expertis...

Land use is constantly changing, and water plays a critical role in the process. If changes are noticed quickly or are predictable, land use planning and policies can be devised to mitigate almost any problem. Accordingly, researchers present a mask region-based convolutional neural network (Mask R-CNN) for water body segmentation from aerial image...

For handwritten character recognition, a common problem is that each writer has unique handwriting for each character (e.g. stroke, head, loop, and curl). The similarities of handwritten characters in each language is also a problem. These similarities have led to recognition mistakes. This research compared deep Convolutional Neural Networks (CNNs...

Thai silk fabrics have unique patterns in different regions of Thailand. The designers may have been inspired and took ideas from the natural environment to create new silk patterns. Hence, many new silk patterns are modified from the original silk pattern. It is challenging for people to recognize a pattern without any prior knowledge and expertis...

The identification process of plant species is one of the significant and challenging problems. In this research area, many researchers have focused on identifying the plant leaf images because the leaves of a plant are found almost all year round. The achieve method of the plant leaf image recognition is based on unique extraction features from th...

The real-world food image is a challenging problem for food image classification, because food images can be captured from different perspective and patterns. Also, many objects can appear in the image, not just foods. To recognize food images, in this paper, we propose a modified MobileNet architecture that is applies the global average pooling la...

The real-world food image is a challenging problem for food image classification, because food images can be captured from different perspective and patterns. Also, many objects can appear in the image, not just foods. To recognize food images, in this paper, we propose a modified MobileNet architecture that is applies the global average pooling la...

Local gradient feature descriptors have been proposed to calculate the invariant feature vector. These local gradient methods are very fast to compute the feature vector and achieved very high recognition accuracy when combined with the support vector machine (SVM) classifier. Hence, they have been proposed to solve many problems in image recogniti...

Building security is crucial, but guards and CCTV may be inadequate for monitoring all areas. A quadcopter (drone) with manual and autonomous control was used in a trial mission in this project. Generally, all drones can stream live video and take photos. They can also be adapted to assist better decision-making in emergencies that occur inside a b...

In this paper, we proposed a face verification method. We experiment with a histogram of oriented gradients description combined with the linear support vector machine (HOG+SVM) as for the face detection. Subsequently, we applied a deep learning method called ResNet-50 architecture in face verification. We evaluate the performance of the face verif...

This paper examines a deep convolutional neural network (Deep CNN) for plant recognition in a natural environment. The primary objective is comparing 4 CNN architectures includes LeNet-5, AlexNet, GoogLeNet, and VGGNet on three plant datasets; PNE, 102 Flower, and Folio. The images in the PNE and 102 Flower dataset can be with a complicated backgro...

In this paper, we propose to use deep convolutional neural network (CNN) architectures, namely the deep residual networks (ResNet), the GoogLeNet, the AlexNet, and the AlexNet architectures, for pornographic image dataset. Also, the local descriptors, namely the local binary patterns (LBP), the histogram of oriented gradients, and the scale invaria...

This research aims to present the method for identifying distributed denial of service (DDoS) attacks. Two benchmark dataset, including KDD CUP 1999 and NSL-KDD, were used. The dataset were checked and deleted duplicate data. After the process, the amount of records of KDD Cup 1999 dataset were decreased from 4,898,431 records to 529,655 records, a...

We propose a people and object tracking algorithm for an autonomous unmanned aerial vehicle (UAV). It uses as a surveillance camera and can move anywhere. The camera from UAV is not fixed location as closed-circuit television. The face detection and objection detection are applied to support our proposed. In this research, the UAV model for this pa...

In order to implement information and communication technology (ICT) successfully, it is important to understand the underlying factors that influence Agricultural adoption. Therefore, this research intends to study this perspective of factors that influence and impact successful ICT adoption and related agricultural performance. Case study and sur...

This paper examines a deep convolutional neural network (Deep CNN) for plant recognition in the natural environment. The primary objective was to compare 4 CNN architectures including LeNet-5, AlexNet, GoogLeNet, and VGGNet on three plant datasets; PNE, 102 Flower, and Folio. The images in the PNE and 102 Flower dataset include a complicated backgr...

This paper aims to do a comparative study of local feature descriptor techniques and convolutional neural networks (CNN) for retrieving Thai silk pattern images. Two feature descriptor techniques, the histogram of oriented gradients and the scale-invariant feature transform, are compared to extract feature vectors from the silk pattern images. We c...

In this paper, we propose a local descriptors approach to classify pornographic images. Two local descriptors including the scale-invariant feature transform (SIFT) and the histogram of oriented gradients (HOG) are computed feature vectors from pornographic images. The extracted features are supplied to the K-Nearest Neighbor Algorithm and Support...

The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (CNNs) on three plant datasets; AgrilPlant, LeafSnap...

Most research in image classification has focused on applications such as face, object, scene and character recognition. This paper examines a comparative study between deep convolutional neural networks (CNNs) and bag of visual words (BOW) variants for recognizing animals. We developed two variants of the bag of visual words (BOW and HOG-BOW) and...

Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and artificial intelligence. There are already differen...

The use of k-Nearest Neighbors (kNN) algorithm for recognizing handwritten character scripts is presented in this survey paper. The kNN algorithm, the simple and well-known algorithm for machine learning, is suitable for classification scheme. According to the kNN algorithm, unknown data is firstly compared with the training samples to compute the...

Face identification under small sample conditions is currently an active research area. In a case of very few reference samples, optimally exploiting the training data to make a model which has a low generalization error is an important challenge to create a robust face identification algorithm. In this paper we propose to combine the histogram of...

The Single Sample per Person Problem is a challenging problem for face recognition algorithms. Patch-based methods have obtained some promising results for this problem. In this paper, we propose a new face recognition algorithm that is based on a combination of different histograms of oriented gradients (HOG) which we call Multi-HOG. Each member o...

In this paper we propose the use of several feature extraction methods, which have been shown before to perform well for object recognition, for recognizing handwritten characters, These methods are the histogram of oriented gradients (HOG), a bag of visual words using pixel intensity information (BOW), and a bag of visual words using extracted HOG...

In face recognition, face rotation alignment is an important part of the recognition process. In this paper, we present a hierarchical detector system using eye and eye-pair detectors combined with a geometrical method for calculating the in-plane angle of a face image. Two feature extraction methods, the restricted Boltzmann machine and the histog...

In this paper we propose the use of several feature extraction methods, which have been shown before to perform well for object recognition, for recognizing handwritten characters. These methods are the histogram of oriented gradients (HOG), a bag of visual words using pixel intensity information (BOW), and a bag of visual words using extracted HOG...

The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the computational complexity of the kernelized versio...

This paper describes the use of a novel A* path-planning algorithm for performing line segmentation of handwritten documents. The novelty of the proposed approach lies in the use of a smart combination of simple soft cost functions that allows an artificial agent to compute paths separating the upper and lower text fields. The use of soft cost func...

We propose a novel handwritten character recog-nition method for isolated handwritten Bangla digits. A feature is introduced for such patterns, the contour angular technique. It is compared to other methods, such as the hotspot feature, the gray-level normalized character image and a basic low-resolution pixel-based method. One of the goals of this...

We propose a novel handwritten character recog-nition method for isolated handwritten Bangla digits. A feature is introduced for such patterns, the contour angular technique. It is compared to other methods, such as the hotspot feature, the gray-level normalized character image and a basic low-resolution pixel-based method. One of the goals of this...

Feature extraction techniques can be important in character recognition, because they can enhance the efficacy of recognition in comparison to featureless or pixel-based approaches. This study aims to investigate the novel feature extraction technique called the hotspot technique in order to use it for representing handwritten characters and digits...

Feature extraction techniques can be important in character recognition, because they can enhance the efficacy of recognition in comparison to featureless or pixel-based approaches. This study aims to investigate the novel feature extraction technique called the hotspot technique in order to use it for representing handwritten characters and digits...

The purpose of the research is to study the optimization of line segmentation techniques for Thai handwritten documents. This research considered only single-column of Thai documents. The author proposed two new techniques including comparing Thai character and sorting and distinguishing. These two techniques were used with recognized techniques on...