Two Texture Segmentation of Document Image Using Wavelet Packet Analysis

Lee, Geum-Boon; Odoyo, Wilfred O.; Lee, Jae‐Hoon; Chung, Ilyong; Cho, Beom-Joon

doi:10.1109/icact.2007.358379

Cited by 5 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we have shown the results obtained by the three methods: K-means clustering [8], [9], fisher classifier using matched wavelet [11], and our proposed algorithm based on the fuzzy classifier. In the results, regions painted black indicate the text area and regions painted white are non-text areas.…”

Section: A Text/non-text Classification Resultsmentioning

confidence: 99%

“…The proposed feature extraction method is simple, effective with low computational requirements. Compared to other existing methods [8], [9] and [11], feature dimensionality, and the computation of the feature space, is considerably reduced. We have applied our algorithm on several document images with different characteristics and complex backgrounds.…”

Section: Discussionmentioning

confidence: 98%

“…Then real text regions were recognized based on k-means clustering using feature vectors obtained from the Mband image. Geum-Boon Lee et al [9] used an algorithm based on local energy estimation in wavelet packet domain and k-means classifier. In another research, a technique for segmentation of an image into printed text, handwritten text and noise has been proposed in [10] which uses Fisher classifier for the classification task.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Document image segmentation using fuzzy classifier and the dual-tree DWT

Saeedi

Safabakhsh

Mozaffari

2009

2009 14th International CSI Computer Conference

View full text Add to dashboard Cite

In this paper, we propose a new method for textual areas extraction of an image using a fuzzy classifier and dual-tree discrete wavelet transform. We have extended our text extraction scheme for classification of document images into text, background, and picture components. Three class fuzzy classifiers and a morphological post-processing operation is used for this purpose. The proposed method shows better results compared to the previous wavelet-based document image segmentation algorithms.

show abstract

Section: A Text/non-text Classification Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Document image segmentation using fuzzy classifier and the dual-tree DWT

Saeedi

Safabakhsh

Mozaffari

2009

2009 14th International CSI Computer Conference

View full text Add to dashboard Cite

show abstract

“…But the text should be on the concolorous backgrounds, otherwise the text is very difficult to be extracted. Geum-Boon Lee [6] presents a text segmentation method using wavelet packet analysis and k-means clustering algorithm. But it does not do well to some sparse text images.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Text Location and Segmentation

Wen

Huang

Yang

et al. 2009

2009 WRI World Congress on Software Engineering

View full text Add to dashboard Cite

Recently, with the development of the information technology, the number of images is increasing rapidly. There is much important text in these images. In order to satisfy the need of images viewing, text identification and text retrieval, this paper presents an efficient Method for text location and segmentation. Firstly, the image is converted into wavelet transform domain. Then three sub-band wavelet images of HxLy, LxHy, HxHy are binarizated and merged into a texture image by linear combination. The texture image is processed by CRLA(Constrained Run Length Algorithm) and image smoothing to enhance the candidate text regions. Finally, the text regions are located after 8-connected component growth and filtering out non-text regions. In order to be recognized by OCR(Optical Character Recognition), characters are segmented from text blocks based on the mean values of some slices of text-block. Experimental results show that the proposed method is robust to overlapped complex background.

show abstract

“…Kundu and Acharyya [3] proposed a scheme for text-graphics segmentation based on wavelet scale-space features followed by k-means clustering. Lee et al [4] used an algorithm based on local energy estimation in wavelet packet domain and k-means clustering.…”

Section: Introductionmentioning

confidence: 99%

Rough-Fuzzy Clustering and M-Band Wavelet Packet for Text-Graphics Segmentation

Maji

Roy

Kundu

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions. It assumes that the text and non-text regions of a given document are considered to have different textural properties. The M -band wavelet packet is used to extract the scale-space features, which is able to zoom it onto narrow band high frequency components of a signal. A scale-space feature vector is thus derived, taken at different scales for each pixel in an image. Finally, the rough-fuzzy-possibilistic c-means algorithm is used to address the uncertainty problem of document segmentation. The performance of the proposed technique, along with a comparison with related approaches, is demonstrated on a set of real life document images.

show abstract

Two Texture Segmentation of Document Image Using Wavelet Packet Analysis

Cited by 5 publications

References 6 publications

Document image segmentation using fuzzy classifier and the dual-tree DWT

Document image segmentation using fuzzy classifier and the dual-tree DWT

An Efficient Method for Text Location and Segmentation

Rough-Fuzzy Clustering and M-Band Wavelet Packet for Text-Graphics Segmentation

Contact Info

Product

Resources

About