<title>Document image binarization based on texture analysis</title>

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.

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.

In Traditional Chinese Medicine, the human tongue is one of the important organs which contain the information of health status. In order to achieve an automatic tongue diagnostic system, an effective segmentation method for detecting the edge of tongue is very important. We mainly compare the Canny, Snake and threshold (Otsu's thresholding algorithm) methods for edge segmentation. The segmentation using Canny algorithm may produce many false edges after cutting; thus, it is not suitable for use. The Snake segmentation in tongue requires a larger convergence number and spends too much time. The threshold method using Otsu's thresholding algorithm and filtering process can achieve an easy, fast and effective segmentation result in tongue diagnosis. Therefore, it may be useful in clinical automated tongue diagnosis system.

This paper presents a new algorithm to threshold document images. The proposed algorithm deal with complex background images, illumination and aspect variants, back-to-front interference, variation of brightness and different positioned shadows. The algorithm have two phases. The first one uses edge detection and morphological operations to identify the text on the image. The second phase uses the positions of the text to define the threshold value in an adaptive process. Our approach presents promising results in images with complex background released from the Document Image Binarization Contest (DIBCO) when compared with other literature and competition thresholding algorithms.

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.

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.

Document image binarization, especially old handwritten documents, is a very important yet challenging task. There are various bottlenecks for binarizing historical documents due to different types of degradation present imultaneously such as back impression, ink bleed through, faded colours, and wear and tear of the writing media. We consider these degradation as various types of noise in the document image. Here we have proposed a 2D morphological network which consists of basic morphological operation like dilation and erosion to perform our targeted task. The network also includes linear combination of output from dilation and erosion operations. The aforementioned 2D morphological network is applied for image binarization, where the structuring elements (SEs) and the weights of the linear combination layer are learned through back-propagation. The proposed network has been evaluated on DIBCO 2017 and H-DIBCO 2018 and ISI-Letter dataset. Our results show more convincing as compared to the results of other state-of-the-art methods. Though the network is developed for old handwritten documents, it may be tuned to work for image processing task. The source code can be found here https://github.com/ranjanZ/ICDAR_Binarization.

Binarization is an important step in reading text documents automatically through optical character recognition. Old document images often suffer from degradations that make their binarization a challenging task. In this paper, a new binarization technique for degraded document images is presented. The proposed technique is based on active contours evolving according to intrinsic geometric measures of the document image. The image contrast that is defined by the local image maximum and minimum is used to automatically generate the initialization map of our active contour model; an average thresholding is also used to produce the final delineation and binarization. The proposed implementation benefits from the level set framework, which allows the simultaneous application of a large variety of forces at the stroke–background interface. Our binarization method involves the combination of those forces in a specific way. The efficiency of the proposed method is shown on both recent and historical document images of the Document Image Binarization Contest (DIBCO) datasets that include different types of degradations. The results are compared to a number of known techniques from the literature.

In this paper we present a physical structure detection method for historical handwritten document images. We considered layout analysis as a pixel labeling problem. By classifying each pixel as either periphery, background, text block, or decoration, we achieve high quality segmentation without any assumption of specific topologies and shapes. Various color and texture features such as color variance, smoothness, Laplacian, Local Binary Patterns, and Gabor Dominant Orientation Histogram are used for classification. Some of these features have so far not got many attentions for document image layout analysis. By applying an Improved Fast Correlation-Based Filter feature selection algorithm, the redundant and irrelevant features are removed. Finally, the segmentation results are refined by a smoothing post-processing procedure. The proposed method is demonstrated by experiments conducted on three different historical handwritten document image datasets. Experiments show the benefit of combining various color and texture features for classification. The results also show the advantage of using a feature selection method to choose optimal feature subset. By applying the proposed method we achieve superior accuracy compared with earlier work on several datasets, e.g., We achieved 93% accuracy compared with 91% of the previous method on the Parzival dataset which contains about 100 million pixels.

In this study, a method of thresholding is proposed that based on the histogram shape of each image, proposes the more appropriate technique from the famous and common thresholding techniques. Thresholding, which is a commonly used operation for image processing, is the selection of one of the image pixels that determines the border for background and foreground of the image. After determining a suitable threshold, the image can be converted to a binary image and from this binary image, which has a very small size, informations can be extracted for utilization in various scientific subjects. From histogram tests of images, examination of criteria extracted from the histogram shape of each image, and empirical knowledge, we designed an expert system that proposes the suitable thresholding technique for an image based on the histogram of that image. We used modeling and matching for designing this system in such way that a model was produced from the histogram of the most suitable result for each thresholding technique and was stored on a knowledge base. Afterwards, a system is coded to receive the input image and after matching this image with the above models, the technique with the most model proximity to the histogram of the input image is proposed as the more appropriate technique from the methods of thresholding. Nowadays thresholding, which is the preprocessing for each image process, is widely used. So far, many techniques and methods of thresholding have been proposed. Although all the techniques are useful, the results vary for each image. Sometimes a certain technique is more appropriate for an image than the rest of the techniques. Based on experimentation and qualitative reasoning which will be mentioned later, we concluded what techniques are more appropriate for different shapes of histograms. The application of the proposed method is to facilitate selection of a suitable thresholding technique for an image. Furthermore, the proposed method was tested on many images and the results showed that by using this method, choosing a suitable thresholding method can be greatly automatized.

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a question. At present, however, little research has considered the effect of time or conducted experiments with data from different months. To identify the best index for surface water body extraction, two regions in Jilin Province were selected for the case study and a comprehensive comparative analysis considering the imagery acquisition time was conducted. Ten water indices were selected and calculated based on the 30 m spatial resolution Landsat TM/OLI imagery acquired in 1999 and 2001 and 2019 and 2021 from May to October. The indices included the Modified Normalized Difference Water Index (NDWI3 and MNDWI), Automated Water Extraction Index (AWEI) for images with and without shadow, Multi-Band Water Index (MBWI), New Water Index (NWI), Water Ratio Index (WRI), Sentinel-2 Water Index (SWI) originally calculated based on the Sentinel-2 imagery, New Comprehensive Water Index (NCIWI), Index of Water Surfaces (IWS), and Enhanced Water Index (EWI). The OTSU algorism was adopted to adaptively determine the optimal segmentation threshold for each index and the indices were compared in terms of inter-class separability, threshold sensitivity, optimal threshold robustness, and water extraction accuracy. The result showed that NWI and EWI performed the best in different months and years, with the best water enhancement effect that could suppress background information, especially for the water-related land use types and cloud pollution. Their optimal segmentation thresholds throughout the study period were more stable than others, with the ranges of −0.342 to −0.038 and −0.539 to −0.223, respectively. Based on the optimal thresholds, they achieved overall accuracies of 0.952 to 0.981 and 0.964 to 0.981, commission errors of 0 to 28.2% and 0 to 7.7%, and omission errors of 0 to 15% and 0 to 8%, with a kappa coefficient above 0.8 indicating good extraction results. The results demonstrated the effectiveness of NWI and EWI combined with the OTSU algorithm in better monitoring surface water during different water periods and offering reliable results. Even though this study only focuses on the lakes within two regions, the indices have the potential for accurately monitoring the surface water over other regions.

This paper presents the basic thresholding techniques using Simulink and DSP builder tool for removing the show-through noise from Document images using Altera Cyclone® V FPGA kit. Initially, show-through noise from the document images was removed using the thresholding method in the OpenCV environment and compared the results with the FPGA results. Then, we created a library file for Cyclone® V 5CSEMA5F31C6N series, as board block in DSP builder. We have designed, developed and implemented four methods of thresholding techniques using Simulink and DSP builder. The OpenCV software and Altera DE1-SoC board hardware results were compared qualitatively and quantitatively. DSP builder and Simulink make complex tasks like image processing and reduce the complications in designing the structure of algorithm, which pave the way for hardware co-simulation.

Image thresholding is a widely used technology for a lot of computer vision applications, and among various global thresholding algorithms, Otsu‐based approaches are very popular due to their simplicity and effectiveness. While the usage of Otsu‐based thresholding methods is well discussed, the performance analyses of these methods are rather limited. In this paper, we first review nine Otsu‐based approaches and categorize them based on their objective functions, preprocessing, and postprocessing strategies. Second, we conduct several experiments to analyze the model characteristics using different scene parameters both on synthetic images and real‐world cell images. We put more attention to examine the variance of foreground object and the effect of the distance between mean values of foreground and background. Third, we explore the robustness of algorithms by introducing two typical kinds of noises under different intensities and compare the running time of each method. Experimental results show that NVE, WOV, and Xing’s methods are more robust to the distance of mean values of foreground and background. The large foreground variance will cause a larger threshold value. Experiments on cell images show that foreground miss detection becomes serious when the intensities of foreground pixels change drastically. We conclude that almost all algorithms are significantly affected by Salt&Pepper and Gaussian noises. Interestingly, we find that ME increases almost linearly with the intensity of Salt&Pepper noise. In terms of algorithms’ time cost, methods with no preprocessing and postprocessing steps have more advantages. All these findings can serve as a guideline for image thresholding when using Otsu‐based thresholding approaches.

Towards Removal of Shadows Caused due to Object Interferences in Smartphone Captured Document Images using Multiple Mask Generation Technique

Binarization of document images with poor contrast, strong noise, complex patterns, and variable modalities in the gray-scale histograms is a challenging problem. A new binarization algorithm has been developed to address this problem for personal cheque images. The main contribution of this approach is optimizing the binarization of a part of the document image that suffers from noise interference, referred to as the Target Sub-Image (TSI), using information easily extracted from another noise-free part of the same image, referred to as the Model Sub-Image (MSI). Simple spatial features extracted from MSI are used as a model for handwriting strokes. This model captures the underlying characteristics of the writing strokes, and is invariant to the handwriting style or content. This model is then utilized to guide the binarization in the TSI. Another contribution is a new technique for the structural analysis of document images, which we call “Wavelet Partial Reconstruction” (WPR). The algorithm was tested on 4,200 cheque images and the results show significant improvement in binarization quality in comparison with other well-established algorithms.