• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.19-28
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• 2020

Vehicle license plate recognition system is not generalized in Malaysia due to the loose character layout rule and the varying number of characters as well as the mixed capital English characters and italic English words. Because the italic English word is hard to segmentation, a separate method is required to recognize in Malaysian license plate. In this paper, we propose a mixed character level and word level English license plate recognition algorithm using deep learning neural networks. The difference of Gaussian method is used to segment character and word by generating a black and white image with emphasized character strokes and separated touching characters. The proposed deep learning neural networks are implemented on the LPR system at the gate of a building in Kuala-Lumpur for the collection of database and the evaluation of algorithm performance. The evaluation results show that the proposed Malaysian English LPR can be used in commercial market with 98.01% accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1126-1139
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• 1994

This paper proposes an effective recognition system which recognizes the mixed document consisting of handwritten korean/alphanumeric texts and graphic images. In the preprocessing step, an input image is binarized by the proposed thresholding scheme, then graphic and character regions are separated by using connected components and chain codes. Separated Korean characters are merged based on partial recognition and their character types and sized. In the character recognition step, we use the branch and bound algorithm based on DP matching costs to recognize Korean characters. Also we recognize alphanumeric characters using several robust features. Finally we use a dictionary and information of a recognition step to correct wrong recognition results. Computer simulation with several test documents shows what the proposed algorithm recognized effectively handwritten mixed texts.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1183-1186
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• 2004

In the steel production line, the molten metal of a furnace is transformed into billet and then moves to the heating furnace of the hot rolling mill. This paper describes about the realtime billet characters recognition system in the steel production line. Normally, the billets are mixed at yard so that their identifications are very difficult and very important processing. The character recognition algorithm used in this paper is base on the subspace method by K-L transformation. With this method, we need no special feature extraction steps, which are usually error prone. So the gray character images are directly used as input vectors of the classifier. To train the classifier, we have extracted eigen vectors of each character used in the billet numbers, which consists of 10 arabia numbers and 26 alphabet aharacters, which are gathered from billet images of the production line. We have developed billet characters recognition system using this algorithm and tested this system in the steel production line during the 8-days. The recognition rate of our system in the field test has turned out to be 94.1% (98.6% if the corrupted characters are excluded). In the results, we confirmed that our recognition system has a good performance in the poor environments and ill-conditioned marking system like as steel production plant.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1431-1434
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• 2005

In the steel production line, the molten metal of a furnace is transformed into billet and then moves to the heating furnace of the hot rolling mill. This paper describes about the development of recognition system for the characters, which was marked at the billet material by use template-marking plate and hand written method, in the steel plant. For the recognition of template-marked characters, we propose PSVM algorithm. And for the recognition of hand written character, we propose combination methods of CCD algorithm and PSVM algorithm. The PSVM algorithm need some more time than the conventional KLT or SVM algorithm. The CCD algorithm makes shorter classification time than the PSVM algorithm and good for the classification of closed curve characters from Arabic numerals. For the confirmation of algorithm, we have compared our algorithm with conventional methods such as KLT classifier and one-to-one SVM. The recognition rate of experimented billet characters shows that the proposing PSVM algorithm is 97 % for the template-marked characters and combinational algorithm of CCD & PSVM is 95.5 % for the hand written characters. The experimental results show that our proposing method has higher recognition rate than that of the conventional methods for the template-marked characters and hand written characters. By using our algorithm, we have installed real time character recognition system at the billet processing line of the steel-iron plant.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.9
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• pp.14-22
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• 2015

TIn this paper, a centering method for an unfocused input character using the spherical domain system and the centering character to use the shift invariant feature for the recognition system is proposed. A system for recognition is implemented using the centroid method with coordinate average values, and the results of an above 78.14% average differential ratio for the character features were obtained. It is possible to extract the shift invariant feature using spherical transformation similar to the human eyeball. The proposed method, which is feature extraction using spherical coordinate transform and transformed extracted data, makes it possible to move the character to the center position of the input plane. Both digital and optical technologies are mixed using a spherical coordinate similar to the 3 dimensional human eyeball for the 2 dimensional plane format. In this paper, a centering character feature using the spherical domain is proposed for character recognition, and possibilities for the recognized possible character shape as well as calculating the differential ratio of the centered character using a centroid method are suggested.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1977-1985
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• 2007

The character recognition technique is one of the artificial intelligence and has been widely applied in the automated system robot HCI(Human Computer Interaction), etc. This paper introduces the character set and the representative character that can be used in the recognition of the mage ROI. The character codes in this ROI include the digit, symbol, English and Hereat etc. We proposed the efficient multi-classifier structure by combining the small-size classifiers hierarchically. Moreover, we generated each small-size classifiers by delta-bar-delta learning algorithm. We tested the performance with various kinds of images and achieved the accuracy of 99%. The proposed multi-classifier showed the efficiency and the reliability for the mixed character code set.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3265-3276
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• 1996

The studies of document recongnition have been focused mainly on Korean documents. But most of documents composed of Korean and other characters. So, in this paper, we propose the document recognition system that can recognize the multi-size, multi font and mixed type characters. We have utilized a large scale network model, "CombNET" which consists of a 4 layered network with combstructure. And we propose recognition method that can recognize characters without discrimination of character type. The first layer constitutes a Kohonen's SOFM network which quantizes an input feature vector space into several sub-spaces and the following 2-4 layers constitutes BP network modules which classify input data in each sub-space into specified catagories. An experimental result demonstrated the usefulness of this approach with the recognition rates of 95.6% for the training data. For the mixed type character documents we obtained the recognition rates of 92.6% and recognition speed of 10.3 characters per second.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.215-222
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• 2007

As Information Technology growing, the character recognition application plays an important role in the ubiquitous environment. In this paper, we propose the Modified ART1 network using Multiresolution Mergence to the problems of the character recognition. The approach is based on the unsupervised neural network and multiresolution. In order to decrease noises and to increase the classification rate of the characters, we propose the multiresolution mergence strategy using both high resolution and low resolution information. Also, to maximize the effect of multiresolution mergence, we use a modified ART1 method with a different similarity measure. Our experimental results show that the classification rate of character is quite increased as well as the performance of the propose algorithm in conjunction with the similarity measure is improved comparing to the conventional ART1 algorithm in this application.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.699-707
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• 2015

Although digit character recognition has got a significant improvement in recent years, it is still challenging to achieve satisfied result if the data contains an amount of distracting factors. This paper proposes a novel digit character recognition approach using a multi-layer hierarchical model, Hybrid Restricted Boltzmann Machines (HRBMs), which allows the learning architecture to be robust to background distracting factors. The insight behind the proposed model is that useful high-level features appear more frequently than distracting factors during learning, thus the high-level features can be decompose into hybrid hierarchical structures by using only small label information. In order to extract robust and compact features, a stochastic 0-1 layer is employed, which enables the model's hidden nodes to independently capture the useful character features during training. Experiments on the variations of Mixed National Institute of Standards and Technology (MNIST) dataset show that improvements of the multi-layer hierarchical model can be achieved by the proposed method. Finally, the paper shows the proposed technique which is used in a real-world application, where it is able to identify digit characters under various complex background images.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3584-3591
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• 2000

In this paper we propose measure for performance evaluationof character recognition, We used three commercial character recognizers and one laboratory character recognizer for test. The characteristics of each recognizer is compared by proposed evaluation standrd, and analyzed characteristrics For the input test data, KT test collection are used. KT test collection is composed of 1000 document images about and complete source text. In this paper we propose method for measuring recognition rage in character unit for evaluation of character recogrition, The recogrition rates are compared and analyzed by single feature characteristic or mixed feature characteristic.