Restoration based Contourlet Transform for historical document image binarization

Document image binarization plays an important role in image segmentation and its effect directly impacts on the quality of the OCR recognition system. However, binarization is difficult for camera-based document images with poor contrast or illumination. In this paper, we propose a binarization algorithm, called NFCM, for camera-based document image. NFCM, a local threshold method, is a combination of Niblack algorithm and FCM (Fuzzy C-Means) algorithm. It is good at not only preserving the character stokes, but also alleviating the ghost artifacts. Comparative experiments show that NFCM can obtain favorable results with respect to the OCR performance.

Document image binarization is a crucial step towards optical character recognition and analysis. One common way to achieve image binarization is thresholding. Thresholding methods can be divided into global and local ones in terms of the regional information used in obtaining the threshold values. Both methods have their respective drawbacks. Global methods can not adapt to background variations while local methods have the problem of local widow size determination. Hybrid methods that combines both local and global thresholds and can alleviating these drawbacks. In this paper, the hybrid threshold method is utilized and trade-off between the local and global contents is determined using variational optimization. The proposed algorithm is tested on (H-)DIBCO benchmarks and has shown superior performance to one state-of-the-art document image binarization method.

This study aims to investigate the different local thresholding methods on various regions of noise images, which could be used for image binarization of optical coherence tomography images. In the methods one hundred 8-bit images of noise, 1000x1000 pixel in size, is generated using ImageJ/FIJI program. Images processed with four different auto local threshold method in ImageJ/FIJI program as Niblack, mean, median and midgrey, to perform binarization. Twenty-five different region of interest, 100x100 pixel in size, from different region in an image analyzed for area percentage (AP) measurement. Normality tests were performed via Saphiro Wilk Normality test, and Student’s t test and one-way ANOVA were used to assess the continuous variables, and Bonferroni test for post hoc analysis, utilizing the IBM SPSS Statistics for the statistical analysis. In the results of this study mean AP for Niblack method was 42.08 ± 0.32%, for mean method was 50.00 ± 0.32%, for median method was 49.28 ± 0.16%, and for midgrey method was 49.63 ± 2.09%. One-way ANOVA analysis shows all the different subgroups of Niblack and mean, Niblack and median, Niblack and midgrey, mean and median, mean and midgrey, and median and midgrey measurements are significantly different from each other. In conclusion this study examined 100 noise images across 25 regions using four auto local threshold methods (Niblack, mean, median, and midgrey). Analyses indicated that Niblack having the lowest mean and there is significant difference between all the methods; researchers using auto local threshold methods in OCT image processing should select methods aligned with data properties, warranting further exploration of these methods’ impact on diverse OCT image, especially taking into account the effect of the noise.

First-generation contrast agents are used in echocardiography to diagnose PFO (Patent Foramen Ovale). The degree of PFO is based on the number of bubbles found in the left atrium. This paper presents research on the preparation of an algorithm for the segmentation of first-generation contrast bubbles. To fulfill the research objective, the images were first preprocessed to reduce the influence of possible noise and artefacts; subsequently, binarization was performed and the segmentation of bubbles then occurred. The results were evaluated by the Dice coefficient. To obtain the best results, the following local thresholding methods were compared: Median, Sauvola, and SDA. The outcome was evaluated and the best values of the Dice coefficient were obtained with two local thresholding methods: the Median and the Sauvola algorithms.KeywordsSegmentationHeartEchocardiographicPFOContrast

This article deals with binarization of degraded document images. In the proposed approach, Canny edge image of the input degraded document image is obtained after blurring it with a Gaussian filter. Next, the gray values of the two pixels of the input image at the left and right of each edge pixel are noted to form a histogram of these gray values which possesses two distinct peaks and the lowest valley between them provides the global threshold value. Each pixel with gray value greater than the above threshold is turned as background pixel. A small square window is considered around each non-background pixel and certain simple statistics are computed on the gray values of the pixels of this small window based on which the said pixel is turned either background or foreground. Such a local thresholding method at the latter stage can efficiently handle various degradations in the document. The binarized image so obtained is finally subjected to certain common post-processing operations. The proposed method has been compared with a few existing binarization techniques.

Historical documents such as old books and manuscripts have a high aesthetic value and highly appreciated. Unfortunately, there are some documents cannot be read due to quality problems like faded paper, ink expand, uneven colour tone, torn paper and other elements disruption such as the existence of small spots. The study aims to produce a copy of manuscript that shows clear wordings so they can easily be read and the copy can also be displayed for visitors. 16 samples of Jawi historical manuscript with different quality problems were obtained from The Royal Museum of Pahang, Malaysia. We applied three binarization techniques; Otsu's method represents global threshold technique; Sauvola and Niblack method which are categorized as local threshold techniques. We compared the binarized images with the original manuscript to be visually inspected by the museum's curator. The unclear features were marked and analyzed. Most of the examined images show that with optimal parameters and effective pre processing technique, local thresholding methods are work well compare with the other one. Niblack's and Sauvola's techniques seem to be the suitable approaches for these types of images. Most of binarized images with these two methods show improvement for readability and character recognition. For this research, even the differences of image result were hard to be distinguished by human capabilities, after comparing the time cost and overall achievement rate of recognized symbols, Niblack's method is performing better than Sauvola's. We could improve the post processing step by adding edge detection techniques and further enhanced by an innovative image refinement technique and a formulation of a class proper method.

Low brightness contrast and grey level discontinuities of the ultrasonic liver image make it difficult to segment the object and the background and to extract the edges of the object using the global optimal threshold method. In this paper, we investigate a local optimal threshold method for the segmentation of ultrasound liver image. First of all, the distributed energy of the ultrasound liver image is estimated in the proposed liver segmentation. Then, the polynomials are fitted from the distributed energy data and a peak zone is defined from the minimum of the fitted polynomials. Finally, a few blocked images are divided from the number of the peak zones. Furthermore, multiple local optimal thresholds are obtained from the blocked images using Otsu’s method, and the ultrasonic liver image is segmented according to all local optimal thresholds. Experimental results validate the segmentation and edge detection of liver in the ultrasound images.

Document image binarization is a central problem in many document analysis systems. Indeed, it represents one of the basic challenges, especially in case of historical documents analysis. In this paper, we propose a novel robust multi stage framework that combines different existing document image thresholding methods for the purpose of getting a better binarization result. CLAHE technique is introduced to significantly enhance contrast in some poor images. The proposed method then uses a hybrid algorithm to partition image into foreground and background. A special procedure is finally applied in order to remove small noise and correct characters morphology. Experimental results prove the accuracy and the efficiency of our approach on document images binarization over three popular datasets compared to some well-known methods in literature.

Pansharpening is a technique that fuses the information of a low-resolution multispectral image and a high-resolution panchromatic image, usually remote sensing images, to provide a high-resolution multispectral image. In the literature, this task has been addressed from different points of view being one of the most popular wavelet-based algorithms. Recently, a new transform, the contourlet transform, has been proposed. This transform combines the advantages of the wavelets transform, with a more efficient directional information representation. The result is a flexible multiscale, multidirection and shift-invariant decomposition that can be efficiently implemented via the a’trous algorithm. In this chapter, we compare the wavelet-based pansharpening with existing contourlet-based approaches and propose a new pansharpening method based on the contourlet transform. The performance of the contourlet in general, and the proposed method in particular, is assessed numerically and visually for Landsat and SPOT images.

This paper proposes an adaptive binarization method for the document image binarization acquired by an imaging phone. The used algorithm determines the local thresholds with the information from the global trend as well as the local details. As a consequence, the proposed method is good not only for preserving the fine details of the character structure, but also for alleviating noise. The effectiveness of the proposed method is illustrated using the results obtained with a Nokia imaging phone.

문서 이미지의 이진화는 문서 인식의 이전 단계에서 주로 사용되며, 이진화의 성공 여부에 따라 문서 인식의 결과에 영향을 미치는 중요한 단계로 볼 수 있다. 지금까지 문서 이미지를 이진화 하기 위한 다양한 기법들이 연구되었지만, 문서 이미지의 상태에 따라 그 결과는 다양하다. 본 논문에서는 객체 추출에 많이 이용되는 MSER(Maximally Stable Extremal Region)을 이용하여 문서 이미지를 이진화하는 기법을 제안한다. 먼저 문서 이미지에서 MSER 객체를 추출한다. 추출된 MSER 객체는 그 자체로 문서 이미지 이진화에 사용되기는 어렵기 때문에 사용하기 적합한 형태로 변경되는 과정을 거친다. 그리고 최종 MSER 객체와 문서 이미지로부터 추출한 대비 이진 이미지를 이용하여 최종 이진 이미지를 계산한다. 실험결과는 본 논문에서 제안한 방법이 문서 이미지의 이진화에 유용함을 보여준다. Document image binarization is largely used as previous stage of document recognition. And the result of document recognition is much affected from the result of document image binarization. There were many studies to binarize document images. The results of previous studies for document image binarization is varied according to the state of document images. In this paper, we propose a technique for document image binarization using MSER that is applied to extract objects from an image. At first, raw MSER objects are extracted from a document image. Because the raw MSER objects cannot be used for document image binarization, the extracted raw MSER objects are modified. Then the final MSER objects are used for document image binarization with the contrast image that is extracted from the document image. Experimental results show that the proposed technique is useful for document image binarization.

A new method based on image analysis for electrospun nanofibre diameter measurement is presented. First, the SEM micrograph of the nanofibre web obtained by electrospinning process is converted to binary image using local thresholding method. In the next step, skeleton and distance transformed image are generated. Then, the intersection points which bring about untrue measurements are identified and removed from the skeleton. Finally, the resulting skeleton and distance transformed image are used to determine fibre diameter. The method is evaluated by a simulated image with known characteristics generated by ?-randomness procedure. The results indicate that this approach is successful in making fast, accurate automated measurements of electrospun fibre diameters.

A new local structural similarity fusion-based thresholding method for homomorphic ultrasound image despeckling in NSCT domain

In order to solve the initial active contour problem of Snake model, Contourlet transform is introduced into the GVF Snake model, which will provides a way to set the initial contour, as a result, will improves the edge detection results of GVF Snake model effectively. Firstly, the contours of the object in images can be obtained based on Contourlet Transform, and this contours will be identified as the initial contour of GVF Snake model. Secondly, then GVF Snake model is used to detect the contour edge of human gait motion. Experimental results show that the proposed method can extract the edge feature accurately and efficiently.

Utilizing the contourlet’s advantages of multiscale, localization, directionality and anisotropy, a New method for multi-focus image fusion using wavelet based contourlet transform and region was proposed. In the contourlet transform, the Laplacian pyramid transform was replaced by the wavelet transform to obtain non-redundant wavelet-based contourlet transform. The source image is decomposed into low-frequency image and high-frequency images by wavelet based contourlet.. The fusion rule of Regional variance is adopted in low-frequency image, and the fusion rule of local energy are adopted in high-frequents subbands. Finally the fused image is obtained through inverse transform. Experimental results show that the proposed method has better subjective visual effect. Compared to traditional gradient pyramid algorithm and wavelet fusion algorithm, the proposed algorithm in paper has smallest mean square error.