• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.806-816
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• 2009

Character recognition system consists of four step; text localization, text segmentation, character segmentation, and recognition. The character segmentation is very important and difficult because of noise, illumination, and so on. For high recognition rates of the system, it is necessary to take good performance of character segmentation algorithm. Many algorithms for character segmentation have been developed up to now, and many people have been recently making researches in segmentation of touching or overlapping character. Most of algorithms cannot apply to the text regions of management number marked on the slab in steel image, because the text regions are irregular such as touching character by strong illumination and by trouble of nozzle in marking machine, and loss of character. It is difficult to gain high success rate in various cases. This paper describes a new algorithm of character segmentation to recognize slab management number marked on the slab in the steel image. It is very important that pre-processing step is to convert gray image to binary image without loss of character and touching character. In this binary image, non-touching characters are simply separated by using vertical projection profile. For separating touching characters, after we use combined profile to find candidate points of boundary, decide real character boundary by using method based on recognition. In recognition step, we remove noise of character images, then recognize respective character images. In this paper, the proposed algorithm is effective for character segmentation and recognition of various text regions on the slab in steel image.

• Smart Media Journal
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• v.4 no.3
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• pp.44-49
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• 2015

Text line segmentation is a preprocessing step in OCR, which can significantly influence the accuracy of document analysis applications. This paper proposes a novel methodology for the text line segmentation of handwritten documents. First, the average width of the connected components is used to form a 1-D Gaussian kernel and a smoothing operation is then applied to the input binary image. The adaptive binarization of the smoothed image forms the final text lines. In this work, the segmentation method involves two stages: firstly, the large connected components are labelled as a unique text line using text line area mapping. Secondly, the final refinement of the segmentation is performed using the Euclidean distance between the text line and small connected components. The group of uniquely labelled text candidates achieves promising segmentation results. The proposed approach works well on Korean and English language handwritten documents captured using a camera.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.161-166
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• 2011

We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user's marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.875-878
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• 2004

Researching in text detection and segmentation has been done for a long period in the OCR area. However, there is some other area that the text detection and segmentation from images can be very useful. In this report, we first propose the design of a mobile translator system which helps non-native speakers to understand the foreign language using ubiquitous mobile network and camera mobile phones. The main focus of the paper will be the algorithm in detecting and segmenting texts embedded in the natural scenes from taken images. The image, which is captured by a camera mobile phone, is transmitted to a translator server. It is initially passed through some preprocessing processes to smooth the image as well as suppress noises. A threshold is applied to binarize the image. Afterward, an edge detection algorithm and connected component analysis are performed on the filtered image to find edges and segment the components in the image. Finally, the pre-defined layout relation constraints are utilized in order to decide which components likely to be texts in the image. A preliminary experiment was done and the system yielded a recognition rate of 94.44% on a set of 36 various natural scene images that contain texts.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1391-1394
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• 1997

This paper proposes a new image processing algorithm to recognize korean documents. It take out the region of text area form input image, then it makes esgmentation of lines, words and characters in the text. A precision segmentation is very important to recognize the input document. The input image has 8-bit gray scaled resolution. Not only the histogram but also brightness dispersion graph are used for segmentation. The result shows a higher accuracy of document recognition.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.275-302
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• 2007

We propose a novel still image compression system which supports a high quality text compression function. The system segments the text from the image and compresses the text with a high quality. The system shows 48:1 high compression ratio using context-based adaptive binary arithmetic coding. The arithmetic coding performs the high compression by the codeblocks in the bitplane. The input of the system consists of a segmentation mode and a ROI(Region Of Interest) mode. In segmentation mode, the input image is segmented into a foreground consisting of text and a background consisting of the remaining region. In ROI mode, the input image is represented by the region of interest window. The high quality text compression function with a high compression ratio shows that the proposed system can be comparable with the JPEG2000 products. This system also uses gray coding to improve the compression ratio.

• International Journal of Contents
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• v.9 no.1
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• pp.1-5
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• 2013

Standard Gaussian Mixture Model (GMM) is a well-known method for image segmentation. However, one of its problems is that we consider the pixel as independent to each other, which can cause the segmentation results sensitive to noise. It explains why some of existing algorithms still cannot segment texts from the background clearly. Therefore, we present a new method in which we incorporate the spatial relationship between a pixel and its neighbors inside $3{\times}3$ windows to segment the text. Our approach works well with images containing texts, which has different sizes, shapes or colors in case of light changes or complex background. Experimental results demonstrate the robustness, accuracy and effectiveness of the proposed model in image segmentation compared to other methods.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.463-470
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• 2010

We presented a very fast and robust method of text line segmentation based on the DCT blocks of color image without decompression and binary transformation processes. Using DC and another three primary AC coefficients from block DCT we created a gray-scale image having reduced size by 8x8. In order to detect and locate white strips between text lines we analyzed horizontal and vertical projection profiles of the image and we applied a direct markov model to recover the missing white strips by estimating hidden periodicity. We presented performance results. The results showed that our method was 40 - 100 times faster than traditional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.120-129
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• 1996

This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.