• Title/Summary/Keyword: text binarization

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The Binarization of Text Regions in Natural Scene Images, based on Stroke Width Estimation (자연 영상에서 획 너비 추정 기반 텍스트 영역 이진화)

  • Zhang, Chengdong;Kim, Jung Hwan;Lee, Guee Sang
    • Smart Media Journal
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    • v.1 no.4
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    • pp.27-34
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    • 2012
  • In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

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A Fast Algorithm for Korean Text Extraction and Segmentation from Subway Signboard Images Utilizing Smartphone Sensors

  • Milevskiy, Igor;Ha, Jin-Young
    • 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.

Stroke Width-Based Contrast Feature for Document Image Binarization

  • Van, Le Thi Khue;Lee, Gueesang
    • Journal of Information Processing Systems
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    • v.10 no.1
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    • pp.55-68
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    • 2014
  • Automatic segmentation of foreground text from the background in degraded document images is very much essential for the smooth reading of the document content and recognition tasks by machine. In this paper, we present a novel approach to the binarization of degraded document images. The proposed method uses a new local contrast feature extracted based on the stroke width of text. First, a pre-processing method is carried out for noise removal. Text boundary detection is then performed on the image constructed from the contrast feature. Then local estimation follows to extract text from the background. Finally, a refinement procedure is applied to the binarized image as a post-processing step to improve the quality of the final results. Experiments and comparisons of extracting text from degraded handwriting and machine-printed document image against some well-known binarization algorithms demonstrate the effectiveness of the proposed method.

A Study on Automatic Binarization of Text Region Using a Stroke Filter (스트록 필터를 이용한 문자영역 이진화에 관한 연구)

  • Jung, Cheol-Kon;Kim, Jong-Kyu
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.2C
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    • pp.178-183
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    • 2008
  • The videotext brings important semantic clues into video content analysis. In this paper, we propose an automatic binarization method of text region using a stroke filter. Proposed text binarization method consists of stroke filtering, text color polarity determination, and local region growing. By using the responses of dark and bright stroke filters, we can determine color polarity of text region automatically. And the method is robust against complex background, because it considers stroke information of videotexts by using a stroke filter. The effectiveness of our method is verified by experiments on a challenging database.

Text Location and Extraction for Business Cards Using Stroke Width Estimation

  • Zhang, Cheng Dong;Lee, Guee-Sang
    • International Journal of Contents
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    • v.8 no.1
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    • pp.30-38
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    • 2012
  • Text extraction and binarization are the important pre-processing steps for text recognition. The performance of text binarization strongly related to the accuracy of recognition stage. In our proposed method, the first stage based on line detection and shape feature analysis applied to locate the position of a business card and detect the shape from the complex environment. In the second stage, several local regions contained the possible text components are separated based on the projection histogram. In each local region, the pixels grouped into several connected components based on the connected component labeling and projection histogram. Then, classify each connect component into text region and reject the non-text region based on the feature information analysis such as size of connected component and stroke width estimation.

The Color Polarity Method for Binarization of Text Region in Digital Video (디지털 비디오에서 문자 영역 이진화를 위한 색상 극화 기법)

  • Jeong, Jong-Myeon
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.9
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    • pp.21-28
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    • 2009
  • Color polarity classification is a process to determine whether the color of text is bright or dark and it is prerequisite task for text extraction. In this paper we propose a color polarity method to extract text region. Based on the observation for the text and background regions, the proposed method uses the ratios of sizes and standard deviations of bright and dark regions. At first, we employ Otsu's method for binarization for gray scale input region. The two largest segments among the bright and the dark regions are selected and the ratio of their sizes is defined as the first measure for color polarity classification. Again, we select the segments that have the smallest standard deviation of the distance from the center among two groups of regions and evaluate the ratio of their standard deviation as the second measure. We use these two ratio features to determine the text color polarity. The proposed method robustly classify color polarity of the text. which has shown by experimental result for the various font and size.

Noise Robust Document Image Binarization using Text Region Detection and Down Sampli (문자 영역 검출과 다운샘플링을 이용한 잡음에 강인한 문서 영상 이진화)

  • Jeong, Jinwook;Jun, Kyungkoo
    • Journal of Korea Multimedia Society
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    • v.18 no.7
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    • pp.843-852
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    • 2015
  • Binarization of document images is a critical pre-processing step required for character recognition. Even though various research efforts have been devoted, the quality of binarization results largely depends on the noise amount and condition of images. We propose a new binarization method that combines Maximally Stable External Region(MSER) with down-sampling. Particularly, we propose to apply different threshold values for character regions, which turns out to be effective in reducing noise. Through a set of experiments on test images, we confirmed that the proposed method was superior to existing methods in reducing noise, while the increase of execution time is limited.

Text extraction from camera based document image (카메라 기반 문서영상에서의 문자 추출)

  • 박희주;김진호
    • Journal of Korea Society of Industrial Information Systems
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    • v.8 no.2
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    • pp.14-20
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    • 2003
  • This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

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Text Detection and Binarization using Color Variance and an Improved K-means Color Clustering in Camera-captured Images (카메라 획득 영상에서의 색 분산 및 개선된 K-means 색 병합을 이용한 텍스트 영역 추출 및 이진화)

  • Song Young-Ja;Choi Yeong-Woo
    • The KIPS Transactions:PartB
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    • v.13B no.3 s.106
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    • pp.205-214
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    • 2006
  • Texts in images have significant and detailed information about the scenes, and if we can automatically detect and recognize those texts in real-time, it can be used in various applications. In this paper, we propose a new text detection method that can find texts from the various camera-captured images and propose a text segmentation method from the detected text regions. The detection method proposes color variance as a detection feature in RGB color space, and the segmentation method suggests an improved K-means color clustering in RGB color space. We have tested the proposed methods using various kinds of document style and natural scene images captured by digital cameras and mobile-phone camera, and we also tested the method with a portion of ICDAR[1] contest images.

Text Detection and Recognition in Outdoor Korean Signboards for Mobile System Applications (모바일 시스템 응용을 위한 실외 한국어 간판 영상에서 텍스트 검출 및 인식)

  • Park, J.H.;Lee, G.S.;Kim, S.H.;Lee, M.H.;Toan, N.D.
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.46 no.2
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    • pp.44-51
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    • 2009
  • Text understand in natural images has become an active research field in the past few decades. In this paper, we present an automatic recognition system in Korean signboards with a complex background. The proposed algorithm includes detection, binarization and extraction of text for the recognition of shop names. First, we utilize an elaborate detection algorithm to detect possible text region based on edge histogram of vertical and horizontal direction. And detected text region is segmented by clustering method. Second, the text is divided into individual characters based on connected components whose center of mass lie below the center line, which are recognized by using a minimum distance classifier. A shape-based statistical feature is adopted, which is adequate for Korean character recognition. The system has been implemented in a mobile phone and is demonstrated to show acceptable performance.