• Journal of Information Processing Systems
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• v.13 no.1
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• pp.174-183
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• 2017

In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into segments of equal pixel counts where the centroid of the character is kept as the origin. Three primitive contour features namely, distance (l), angle (${\theta}$), and arc-tochord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languages-Bangla and English. In general, we observed that our proposed contour features outperform other schemes.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.19-22
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• 2000

This paper presents new on-line handwritten algorithm for continuous alphabet uppercase characters. The algorithm is based on the idea that alphabet uppercase character consists of at most 4 strokes. It tries to determine the maximum output for a recognition result among outputs of four recognizers which have the capacity to discriminate the character using from 1 through 4 stroke information. The recognition module has 4 neural network based recognizers, which can recognize from 1 through 4 stroke character. We also use specialized post-processing techniques for improving the recognition performance. Trained on 440 input data and choosing 390 uppercase words for a recognition test we reached a 92% recognition rate.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.103-110
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• 1990

This paper studies on an on-line recognition of handwritten Hangeul characters using the fuzzy inference. To solve the ambiguity due to the variations of writing style by writes, these handwri-tten characters are recognized by means of the fuzzy inference on the production rule which is generated with every relative position information between strokes. In order to reduce the processing time, a subgroup which is previously classified with the number of strokes of reference characters is selected according to the number of strokes of input character, and the tolerance limit of distances between input character and reference characters of a subgroup is introduced to reduce the reference characters which is applied to the fuzzy inference. Experimental results for handwritten Hanguel charters 3990 by 10 writers show the recognition rate of $99.5{\%}$and the average processing time of 0.4sec/character.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.385-392
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• 2006

This paper proposes a new stroke extraction algorithm for phoneme segmentation, which is one of main techniques for off-line handwritten Hangul recognition. The proposed algorithm extracts vertical, slant, and horizontal strokes from phonemes using run-length. The run-length of vertical or slant strokes becomes the width, and also the number of horizontal run-lengths the width. After extracting horizontal strokes from phonemes, the algorithm links two continuous vertical or slant stokes with run-lengths of the strokes' width to represent the features of a character. The extracted strokes can be utilized to recognize a character, using template matching of strokes, which is being adopted in on-line handwritten Hangul recognition.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.13-22
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• 2024

Currently, with the proliferation of digital devices, the significance of handwritten texts in daily lives is gradually diminishing. As the use of keyboards and touch screens increase, a decline in Korean handwriting quality is being observed across a broad spectrum of Korean documents, from young students to adults. However, Korean handwriting still remains necessary for many documentations, as it retains individual unique features while ensuring readability. To this end, this paper aims to implement an application designed to improve and correct the quality of handwritten Korean script The implemented application utilizes the CRAFT (Character-Region Awareness For Text Detection) model for handwriting area detection and employs the VGG-Feature-Extraction as a deep learning model for learning features of the handwritten script. Simultaneously, the application presents the user's handwritten Korean script's reliability on a syllable-by-syllable basis as a recognition rate and also suggests the most similar fonts among candidate fonts. Furthermore, through various experiments, it can be confirmed that the proposed application provides an excellent recognition rate comparable to conventional commercial character recognition OCR systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.413-435
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• 2018

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1019-1022
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• 1999

In this paper, we analysis the effect of a dictionary in a handwritten Hangul word recognition problem in terms of its size and the length of the words in it. With our experimental results, we can account for the word recognition rate depending not only on character recognition performance, but also much on the amount of the information that the dictionary contains, as well as the reduction rate of a dictionary.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2600-2608
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• 1998

This paper proposes an algorithm for off line recognition of handwritten characters, especially effective for large-set characters such as Korean and Chinese characters. The algorithm is based on a minimum distance dlassification method which is simple and easy to implement but suffers from low recognition performance. Two strategies have been developed to improve its performance; one is multi-stage pre-classification and the other is candicate reordering. Effectiveness of the algorithm has been proven by and experimet with the samples of 574 classes in a handwritten Korean character catabase named PE02, where 86.0% of recognition accuracy and 15 characters per second of processing speed have been obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.887-894
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• 2006

This paper proposes a new method that detects the intersection points of handwritten Hangul strokes using run-length. The method firstly finds the strokes' width of handwritten Hangul characters using both horizontal and vertical run-lengths, secondly extracts horizontal and vertical strokes of a character utilizing the strokes' width, and finally detects the intersection points of the strokes exploiting horizontal and vertical strokes. The analysis of both the horizontal and the vertical strokes doesn't use the strokes' angles but both the strokes' width and the changes of the run-lengths. The intersection points of the strokes become the candidated parts for phoneme segmentation, which is one of main techniques for off-line handwritten Hangul recognition. The segmented strokes represent the feature for handwritten Hangul recognition.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.147-153
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• 1995

In this paper, we propose the position- and size-independent handwritten on-line Korean character recognition system using multilayer neural networks which are trained with error back-propagation learning algorithm and the features of Hanguel consonants and vowels. Starting point, end point, and three vectors from starting point to end point of each stroke of characters inputted from mouse or tablet are applied as inputs of neural networks. If double consonants and vowels are separated by single consonants and vowels, all consonants and vowels have at most four strokes. Therefore, four neural networks learn the consonants and the vowels having each number of strokes. Also, we propose the algorithm of separating the consonants and vowels and constructing a character.