• International journal of advanced smart convergence
• /
• v.8 no.3
• /
• pp.131-137
• /
• 2019

This study proposes a method of learning and recognizing the characteristics that are the classification criteria of Hangul using Mask R-CNN, one of the deep learning techniques, to recognize and classify atypical Hangul characters. The atypical characters on the Hangul signboard have a lot of deformed and colorful shapes beyond the general characters. Therefore, in order to recognize the Hangul signboard character, it is necessary to learn a separate atypical Hangul character rather than the existing formulaic one. We selected the Hangul character '닭' as sample data and constructed 5,383 Hangul image data sets and used them for learning and verifying the deep learning model. The accuracy of the results of analyzing the performance of the learning model using the test set constructed to verify the reliability of the learning model was about 92.65% (the area detection rate). Therefore we confirmed that the proposed method is very useful for Hangul signboard character recognition, and we plan to extend it to various Hangul data.

• Convergence Security Journal
• /
• v.24 no.3
• /
• pp.41-49
• /
• 2024

A tokenizer, which is a key component of the Transformer model, lacks the ability to effectively comprehend numerical data. Therefore, to develop a Transformer-based intrusion detection model that can operate within a real-world network environment by training packet payloads as sentences, it is necessary to convert the hexadecimal packet payloads into a character-based format. In this study, we applied three character encoding methods to convert packet payloads into numeric or character format and analyzed how detection performance changes when training them on transformer architecture. The experimental dataset was generated by extracting packet payloads from PCAP files included in the UNSW-NB15 dataset, and the RoBERTa was used as the training model. The experimental results demonstrate that the ISO-8859-1 encoding scheme achieves the highest performance in both binary and multi-class classification. In addition, when the number of tokens is set to 512 and the maximum number of epochs is set to 15, the multi-class classification accuracy is improved to 88.77%.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.12
• /
• pp.61-67
• /
• 2021

In this paper, we propose a method of extracting and recognizing related information for research from images of the unstructured pulmonary function test papers using character detection and recognition techniques. Also, we develop a post-processing method to reduce the character recognition error rate. The proposed structuring method uses a character detection model for the pulmonary function test paper images to detect all characters in the test paper and passes the detected character image through the character recognition model to obtain a string. The obtained string is reviewed for validity using string matching and structuring is completed. We confirm that our proposed structuring system is a more efficient and stable method than the structuring method through manual work of professionals because our system's error rate is within about 1% and the processing speed per pulmonary function test paper is within 2 seconds.

• Journal of Broadcast Engineering
• /
• v.25 no.5
• /
• pp.776-788
• /
• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• International Journal of Ocean System Engineering
• /
• v.2 no.1
• /
• pp.57-61
• /
• 2012

In this paper we use the Kohonen neural network based Self Organizing Map (SOM) algorithm for Urdu Character Recognition. Kohenen NN have more efficient in terms of performance as compare to other approaches. Classification is used to recognize hand written Urdu character. The number of possible unknown character is reducing by pre-classification with respect to subset of the total character set. So the proposed algorithm is attempt to group similar character. Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result of this paper shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.18 no.4
• /
• pp.1-11
• /
• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.

• Smart Media Journal
• /
• v.9 no.2
• /
• pp.92-98
• /
• 2020

This paper proposes a vehicle license plate recognition system using light weight deep learning models without high-end server. The proposed license plate recognition system consists of 3 steps: [license plate detection]-[character area segmentation]-[character recognition]. SSD-Mobilenet was used for license plate detection, ResNet with localization was used for character area segmentation, ResNet was used for character recognition. Experiemnts using Samsung Galaxy S7 and LG Q9, accuracy showed 85.3% accuracy and around 1.1 second running time.

• Journal of IKEEE
• /
• v.22 no.1
• /
• pp.87-92
• /
• 2018

Like CPUs, regular expression processors that perform regular expression pattern matching using instructions have been proposed recently. Of these, only REMPc provides features for character class matching. In this paper, we propose an architecture for efficient character class matching in a regular expression processor, which use character class bitmap format in a instruction operand field and implement the hard-wired character class comparator for several frequently used character classes. Using the proposed method, most of the character classes used in Snort rule can be represented by an operand or an instruction. Thus, character class matching can be performed more efficiently in the proposed archiecture than in REMPc.

• Journal of Advanced Information Technology and Convergence
• /
• v.10 no.2
• /
• pp.61-72
• /
• 2020

Recently, researches that are using deep learning technology in various fields are being conducted. The fields include geographic map processing. In this paper, I propose a method to infer where the map area included in the image is. The proposed method generates and learns images including a map, detects map areas from input images, extracts character strings belonging to those map areas, and converts the extracted character strings into coordinates through geocoding to infer the coordinates of the input image. Faster R-CNN was used for learning and map detection. In the experiment, the difference between the center coordinate of the map on the test image and the center coordinate of the detected map is calculated. The median value of the results of the experiment is 0.00158 for longitude and 0.00090 for latitude. In terms of distance, the difference is 141m in the east-west direction and 100m in the north-south direction.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.10
• /
• pp.241-249
• /
• 2009

Recently, a number of researches related to video annotation and representation have been proposed to analyze video for searching and abstraction. In this paper, we have presented a method to provide the picture elements of conversational participants in video and the enhanced representation of the characters using those elements, collectively called Character-net. Because conversational participants are decided as characters detected in a script holding time, the previous Character-net suffers serious limitation that some listeners could not be detected as the participants. The participants who complete the story in video are very important factor to understand the context of the conversation. The picture elements for detecting the conversational participants consist of six elements as follows: subtitle, scene, the order of appearance, characters' eyes, patterns, and lip motion. In this paper, we present how to use those elements for detecting conversational participants and how to improve the representation of the Character-net. We can detect the conversational participants accurately when the proposed elements combine together and satisfy the special conditions. The experimental evaluation shows that the proposed method brings significant advantages in terms of both improving the detection of the conversational participants and enhancing the representation of Character-net.