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In this paper, we present a Conditional Random Field (CRF) model to deal with the problem of segmenting handwritten historical document images into different regions. We consider page segmentation as a pixel-labeling problem, i.e., each pixel is assigned to one of a set of labels. Features are learned from pixel intensity values with stacked convolutional autoencoders in an unsupervised manner. The...
Multilevel image segmentation is an important technique and indispensable process in vision inspection on semiconductor packages to sort out defective products from the qualified ones, classify and identify the defect types. Conventional multilevel image segmentation methods are computationally expensive, and lack accuracy and stability. To address this issue, this paper proposes a novel gravitational...
Multilevel image thresholding is a powerful and commonly used technique in image analysis. Conventional image segmentation methods suffer a large amount of computation time and unstable segmentation results. In this paper, we present a multilevel image thresholding method based on fuzzy entropy and modified gravitational search algorithm. Fuzzy entropy based image thresholding is extended to multilevel,...
Over-segmentation is often used in text recognition to generate candidate characters. In this paper, we propose a neural network-based over-segmentation method for cropped scene text recognition. On binarized text line image, a segmentation window slides over each connected component, and a neural network is used to classify whether the window locates a segmentation point or not. We evaluate several...
Text localization in born-digital images is usually performed using methods designed for scene text images. Based on the observation that text strokes in born-digital images mostly have complete contours and the pixels on the contours have high contrast compared with the adjacent non-text pixels, we propose a method to extract candidate text components using local contrast. First, the image is segmented...
In this paper we investigate the importance of individual features for the task of document layout analysis, in particular for the classification of the document pixels. The feature set consists of numerous state-of-the-art features, including color, gradient, and local binary patterns (LBP). To deal with the high dimensionality of the feature set, we propose a cascade of an adapted forward selection...
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,...
In this paper we propose a novel hybrid feature selection method for historical Document Image Analysis (DIA). Adapted greedy forward selection and genetic selection are used in a cascading way. We apply the proposed method to the task of historical document layout analysis on three handwritten datasets of diverse nature. The documents contain complex layouts, different handwriting styles, and several...
In this paper we present a novel text line segmentation method for historical manuscript images. We use a pyramidal approach where at the first level, pixels are classified into: text, background, decoration, and out of page, at the second level, text regions are split into text line and non text line. Color and texture features based on Local Binary Patterns and Gabor Dominant Orientation are used...
Page segmentation is still a challenging problem due to the large variety of document layouts. Methods examining both foreground and background regions are among the most effective to solve this problem. However, their performance is influenced by the implementation of two key steps: the extraction and selection of background regions, and the grouping of background regions into separators. This paper...
Various types of degradations such as uneven illumination, shadows, low contrast, smears and heavy noise densities often make thresholding of the document images a difficult job. In this paper, we describe a new adaptive approach for degraded-document binarization. We use the dilation and erosion in gray-scale image processing; as a result get a new image in which the shadow levels and noise densities...
Motion segmentation for dynamic scene is fundamental in computer vision. The key issue is to estimate number and parameters of transformations simultaneously. However, transformations cannot be measured by general Euclidean distance because of them not lying in vector space. In this paper, we convert transformations into fitness vectors which can be easily measured by cosine similarity. Then we apply...
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