• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.80-87
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• 2024

The Manchu language holds historical significance, but a complete dataset of Manchu script letters for training optical character recognition machine-learning models is currently unavailable. Therefore, this paper describes the process of creating a robust dataset of extracted Manchu script letters. Rather than performing automatic letter segmentation based on whitespace or the thickness of the central word stem, an image of the Manchu script was manually inspected, and one copy of the desired letter was selected as a region of interest. This selected region of interest was used as a template to match all other occurrences of the same letter within the Manchu script image. Although the dataset in this study contained only 4,000 images of five Manchu script letters, these letters were collected from twenty-eight writing styles. A full dataset of Manchu letters is expected to be obtained through this process. The collected dataset was normalized and trained using a simple convolutional neural network to verify its effectiveness.

• Journal of IKEEE
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• v.25 no.1
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• pp.25-31
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• 2021

The conventional credit card number recognition system generally needs a card to be placed in a designated location before its processing, which is not an ideal user experience especially for people with visual impairment. To improve the user experience, this paper proposes a novel algorithm that can automatically detect the location of a credit card number based on the fact that a group of sixteen digits has a fixed aspect ratio. The proposed algorithm first performs morphological operations to obtain multiple candidates of the credit card number with >4:1 aspect ratio, then recognizes the card number by testing each candidate via OCR and BIN matching techniques. Implemented with OpenCV and Firebase ML, the proposed scheme achieves 77.75% accuracy in the credit card number recognition task.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.2
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• pp.37-48
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• 2007

In order to become a mega hub port, major ports all over the world are making every effort to enhance their productivity through efficiency of internal operation and introduction of the state-of-the-art technologies. They are not only installing various kinds of high-technology equipments but also introducing advanced technologies for the development of an effective gate system. Recently thanks to the appearance of RFID (radio frequency identification) and OCR (optical character recognition) technology, major container terminals are stewing up the automation of truck and container identification at the container luminal gate. This study aim to develop an automated gate system for identification task based on RFID and OCR technology. It will make mn effective gate operations in a container terminal.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.968-976
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• 1999

Currently, in the most mail automatic processing centers, after facing and canceling, envelope mail is passed through an Optical Character Recognition/Barcode Sorter(OCR/BS) to read the address and 3 of 5 fluorescent(luminescent) barcode is applied. Normally, 30%∼35% of this mail is rejected. The usual reasons for read failure are poor printing quality of address and barcode, script printing and failure to locate the address. This paper describes a verification system of the postal 3 of 5 customer barcode for solving this problem. The certification system of the 3 of 5 customer barcode consists of barcode verification system and postal address database. The purpose of certification system of the customer barcode verifies the postal 3 of 5 customer barcode and tests matching of mail piece postal address, and retrieves postal code.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.229-236
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• 2011

In this paper we present light weighted English-to-Korean and Korean-to-English mobile translators on smart phones. For natural translation and higher translation quality, translation engines are hybridized with Translation Memory (TM) and Rule-based translation engine. In order to maximize the usability of the system, we combined an Optical Character Recognition (OCR) engine and Text-to-Speech (TTS) engine as a Front-End and Back-end of the mobile translators. With the BLEU and NIST evaluation metrics, the experimental results show our E-K and K-E mobile translation equality reach 72.4% and 77.7% of Google translators, respectively. This shows the quality of our mobile translators almost reaches the that of server-based machine translation to show its commercial usefulness.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1329-1338
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• 2021

There are rarely effective methods to help visually impaired people when they want to know the expiration date of products excepted to only Braille. In this study, we developed an expiration date notification system based on YOLO and OCR for visually impaired people. The handicapped people can automatically know the expiration date of a specific product by using our system without the help of a caregiver, fast and accurately. The proposed system is worked by four different steps: (1) identification of a target product by scanning its barcode; (2) segmentation of an image area with the expiration date using YOLO; (3) classification of the expiration date by OCR: (4) notification of the expiration date by TTS. Our system showed an average classification accuracy of about 86.00% when blindfolded subjects used the proposed system in real-time. This result validates that the proposed system can be potentially used for visually impaired people.

• Journal of IKEEE
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• v.27 no.4
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• pp.610-616
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• 2023

In this paper, we implement an unmanned delivery robot system with Robot Operating System(ROS)-based mobile manipulator, and introduce the technologies employed for the system implementation. The robot consists of a mobile robot capable of autonomous navigation inside the building using an elevator and a Selective Compliance Assembly Robot Arm(SCARA)-Type manipulator equipped with a vacuum pump. The robot can determines the position and orientation for picking up a package through image segmentation and corner detection using the camera on the manipulator. The proposed system has a user interface implemented to check the delivery status and determine the real-time location of the robot through a web server linked to the application and ROS, and recognizes the shipment and address at the delivery station through You Only Look Once(YOLO) and Optical Character Recognition(OCR). The effectiveness of the system is validated through delivery experiments conducted within a 4-story building.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.97-107
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• 2024

Despite the rapid advancement in image recognition technology, achieving perfect digitization of tabular documents and handwritten documents still challenges. The purpose of this study is to improve the accuracy of digitizing the logbook by correcting errors by utilizing associated rules considered during logbook entries. Through this, it is expected to enhance the accuracy and reliability of data extracted from logbook through OCR programs. This model is to improve the accuracy of digitizing the logbook of the training ship "Saenuri" at the Mokpo Maritime University by correcting errors identified after Optical Character Recognition (OCR) program recognition. The model identified and corrected errors by utilizing associated rules considered during logbook entries. To evaluate the effect of model, the data before and after correction were divided by features, and comparisons were made between the same sailing number and the same feature. Using this model, approximately 10.6% of errors out of the total estimated error rate of about 11.8% were identified, and 56 out of 123 errors were corrected. A limitation of this study is that it only focuses on information from Dist.Run to Stand Course sections of the logbook, which contain navigational information. Future research will aim to correct more information from the logbook, including weather information, to overcome this limitation.

• International Journal of Ocean System Engineering
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• v.2 no.1
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• pp.24-31
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• 2012

This paper introduce a technique for Word segmentation for the handwritten recognition of Urdu script. Word segmentation or word tokenization is a primary technique for understanding the sentences written in Urdu language. Several techniques are available for word segmentation in other languages but not much work has been done for word segmentation of Urdu Optical Character Recognition (OCR) System. A method is proposed for word segmentation in this paper. It finds the boundaries of words in a sequence of ligatures using probabilistic formulas, by utilizing the knowledge of collocation of ligatures and words in the corpus. The word identification rate using this technique is 97.10% with 66.63% unknown words identification rate.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.18-29
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• 1994

For the efficient recognition of Korean Alphabets, we proposed the tree structure algorithm which was based on K-tuple NRF-SDF using representative images as training images. Representative images consisted of ECP-SDF images of several consonants or vowels. To reduce the effect of sidelobe in the output correlation plane, we used the representative images as training images and obtained the elements of a vector inner product matrix using the peak value of AMPOF correlation of training images with one another. The proposed algorithm consisted of three main-step containing several substeps. In filter synthesis of each step, representative images were used as training images in the first and the second main-step and each consonant or vowel was used as training images in the third main-step. The performance of this algorithm is demonstrated by computer simulation and optical experiment.