Abstract: Disclosed is a casting furnace featuring submerged, mechanical control of liquid level, which relates to continuous casting and solves the problem of low billet productivity. The casting furnace featuring submerged, mechanical control of liquid level includes a melting furnace, a holding furnace, and a crystallizer, the crystallizer including a chamber for accommodating metal liquid, the holding furnace communicating with the melting furnace and the chamber, respectively, a traction head being provided on the crystallizer, the chamber maintaining communication with the holding furnace, the holding furnace including a liftable immersion device, the immersion device being submerged in the metal liquid to elevate liquid level height in the chamber; the crystallizer having a liquid level detecting device, lift height of the immersion device being adjusted based on a detection signal of the liquid level detecting device such that the liquid level in the chamber is maintained at a preset height.

Abstract: A double-acting switch valve includes a mounting bracket and two mutually backup sealing assemblies disposed on the mounting bracket, each of the two sealing assemblies including a carbon rod and a carbon rod moving assembly, the carbon rod moving assembly being configured to translate, and elevate or lower the carbon rod so as to block or open a metal liquid channel between two metal liquid furnaces. Two sealing assemblies which are mutually replaceable, where when the carbon rod on one of the sealing assemblies is worn down, the other sealing assembly may take its place to block the metal liquid outlet again via the carbon rod; and then the worn-down carbon rod is renewed. This design eliminates the time taken for replacing the carbon rod and can reduce the amount of the metal liquid discharged via the metal liquid outlet during replacement of the carbon rod.

Abstract: A technique is described for table grid detection and separation during the analysis and recognition of documents containing table contents. The technique includes the steps of table detection, grid separation, and table cell extraction. The technique is characterized by the steps of detecting the grid lines of a table using, for example, inverse cell detection, separating noise and touching text from the grid lines, and extracting the cell contents for OCR recognition.

Abstract: A technique is provided for enhancing the background of an original document image. In the case of a black-and-white image, the background color of the original document image is detected, the desired enhanced background color of the original document image is determined from a background pixel value Pb that is in the center of the background color range, and the original document image is enhanced to the desired enhanced background color. However, if the background of the original document image is in color, the technique further includes obtaining color image histograms of red, blue and green colors of the original document image, smoothing the histograms, and comparing the histograms to determine if they have the same shape.

Abstract: A technique is described for table grid detection and separation during the analysis and recognition of documents containing table contents. The technique includes the steps of table detection, grid separation, and table cell extraction. The technique is characterized by the steps of detecting the grid lines of a table using, for example, inverse cell detection, separating noise and touching text from the grid lines, and extracting the cell contents for OCR recognition.

Abstract: A technique is described for table grid detection and separation during the analysis and recognition of documents containing table contents. The technique includes the steps of table detection, grid separation, and table cell extraction. The technique is characterized by the steps of detecting the grid lines of a table using, for example, inverse cell detection, separating noise and touching text from the grid lines, and extracting the cell contents for OCR recognition.

Abstract: A technique is provided for enhancing the background of an original document image. In the case of a black-and-white image, the background color of the original document image is detected, the desired enhanced background color of the original document image is determined from a background pixel value Pb that is in the center of the background color range, and the original document image is enhanced to the desired enhanced background color. However, if the background of the original document image is in color, the technique further includes obtaining color image histograms of red, blue and green colors of the original document image, smoothing the histograms, and comparing the histograms to determine if they have the same shape.

Abstract: A technique is provided for enhancing the background of an original document image. In the case of a black-and-white image, the background color of the original document image is detected, the desired enhanced background color of the original document image is determined from a background pixel value Pb that is in the center of the background color range, and the original document image is enhanced to the desired enhanced background color. However, if the background of the original document image is in color, the technique further includes obtaining color image histograms of red, blue and green colors of the original document image, smoothing the histograms, and comparing the histograms to determine if they have the same shape.

Abstract: An automatic document classification system is described that uses lexical and physical features to assign a class ci?C{c1, c2, . . . , ci} to a document d. The primary lexical features are the result of a feature selection method known as Orthogonal Centroid Feature Selection (OCFS). Additional information may be gathered on character type frequencies (digits, letters, and symbols) within d. Physical information is assembled through image analysis to yield physical attributes such as document dimensionality, text alignment, and color distribution. The resulting lexical and physical information is combined into an input vector X and is used to train a supervised neural network to perform the classification.

Abstract: A method, computer readable medium, and device for reducing speckle in an image by detecting the edges of the image to create an edge detected image, binarizing the edge detected image to create a binary edge image for processing, creating a list, L, of connected components in the binary edge image, creating a list, C, of connected components in list L that are smaller than a predetermined number of pixels, determining noise candidate pixels from the edge detected image that are covered by the connected components in list C, computing a histogram he of the noise candidate pixels, calculating a threshold from the total number of noise candidate pixels, and marking the pixels in the connected components in list C having a pixel intensity smaller than the threshold as noise. The pixels marked as noise may then be removed by setting the pixels marked as noise to a background color of the image.

Abstract: A method and corresponding computing device and computer readable storage media containing instructions for modifying the histogram of a grayscale image to improve contrast by extracting black connected components from the grayscale image that touch at least one of the margins of the grayscale image, computing the histogram of the portion of the grayscale image covered by the extracted black connected components, and updating the histogram of the grayscale image by subtracting the histogram of the portion of the binary image covered by the extracted black connected components from the histogram of the grayscale image or by subtracting a function of number of pixels of the portion of the binary image covered by the extracted black connected components from the histogram of the grayscale image. The function may be a property of a document containing the grayscale image, such as the size of the document.

Abstract: A method, computing device, and associated computer readable storage media containing instructions for binarizing a grayscale image by manually determining a first threshold that yields optimal binarization values to one or more images in a set of images, calculating the histograms of each of the images determined using the first threshold, calculating a set of statistical parameters such as the mean, standard deviation and variance of each histogram, determining a second threshold as a function of the set of statistical parameters, and comparing each pixel of the grayscale image to the second threshold. The second threshold T may be a function of the mean m, standard deviation s and variance v and is calculated by fitting a third degree polynomial curve T=a0+a1m+a2s+a3v, where the coefficients A=[a0 a1 a2 a3]T are found using a minimum mean square error algorithm.

Abstract: A method, device and computer readable storage media for enhancing an image for optical character recognition by detecting the edges of the image to create an edge detected image, binarizing the edge detected image to create a binary edge image for processing, dilating the binary edge image to create a dilated binary edge image, taking the XOR difference between the binary edge image and the dilated binary edge image to obtain a text boundary, superimposing the text boundary on the image and determining the pixels of the image that are covered by the text boundary, calculating the average grayscale value of the pixels of the image that are covered by the text boundary, and setting background pixels of the image to the calculated average grayscale value of the pixels of the image that are covered by the text boundary.

Abstract: A technique is provided for enhancing the background of an original document image. In the case of a black-and-white image, the background color of the original document image is detected, the desired enhanced background color of the original document image is determined from a background pixel value Pb that is in the center of the background color range, and the original document image is enhanced to the desired enhanced background color. However, if the background of the original document image is in color, the technique further includes obtaining color image histograms of red, blue and green colors of the original document image, smoothing the histograms, and comparing the histograms to determine if they have the same shape.

Abstract: A technique is described for table grid detection and separation during the analysis and recognition of documents containing table contents. The technique includes the steps of table detection, grid separation, and table cell extraction. The technique is characterized by the steps of detecting the grid lines of a table using, for example, inverse cell detection, separating noise and touching text from the grid lines, and extracting the cell contents for OCR recognition.

Abstract: A method, device and computer readable storage media for enhancing an image for optical character recognition by detecting the edges of the image to create an edge detected image, binarizing the edge detected image to create a binary edge image for processing, dilating the binary edge image to create a dilated binary edge image, taking the XOR difference between the binary edge image and the dilated binary edge image to obtain a text boundary, superimposing the text boundary on the image and determining the pixels of the image that are covered by the text boundary, calculating the average grayscale value of the pixels of the image that are covered by the text boundary, and setting background pixels of the image to the calculated average grayscale value of the pixels of the image that are covered by the text boundary.

Abstract: A method, computing device, and associated computer readable storage media containing instructions for binarizing a grayscale image by manually determining a first threshold that yields optimal binarization values to one or more images in a set of images, calculating the histograms of each of the images determined using the first threshold, calculating a set of statistical parameters such as the mean, standard deviation and variance of each histogram, determining a second threshold as a function of the set of statistical parameters, and comparing each pixel of the grayscale image to the second threshold. The second threshold T may be a function of the mean m, standard deviation s and variance v and is calculated by fitting a third degree polynomial curve T=a0+a1m+a2s+a3v, where the coefficients A=[a0 a1 a2 a3]T are found using a minimum mean square error algorithm.

Abstract: A method, computer readable medium, and device for reducing speckle in an image by detecting the edges of the image to create an edge detected image, binarizing the edge detected image to create a binary edge image for processing, creating a list, L, of connected components in the binary edge image, creating a list, C, of connected components in list L that are smaller than a predetermined number of pixels, determining noise candidate pixels from the edge detected image that are covered by the connected components in list C, computing a histogram he of the noise candidate pixels, calculating a threshold from the total number of noise candidate pixels, and marking the pixels in the connected components in list C having a pixel intensity smaller than the threshold as noise. The pixels marked as noise may then be removed by setting the pixels marked as noise to a background color of the image.

Abstract: A method and corresponding computing device and computer readable storage media containing instructions for modifying the histogram of a grayscale image to improve contrast by extracting black connected components from the grayscale image that touch at least one of the margins of the grayscale image, computing the histogram of the portion of the grayscale image covered by the extracted black connected components, and updating the histogram of the grayscale image by subtracting the histogram of the portion of the binary image covered by the extracted black connected components from the histogram of the grayscale image or by subtracting a function of number of pixels of the portion of the binary image covered by the extracted black connected components from the histogram of the grayscale image. The function may be a property of a document containing the grayscale image, such as the size of the document.

Abstract: An automatic document classification system is described that uses lexical and physical features to assign a class ci?C{c1, c2, . . . , ci} to a document d. The primary lexical features are the result of a feature selection method known as Orthogonal Centroid Feature Selection (OCFS). Additional information may be gathered on character type frequencies (digits, letters, and symbols) within d. Physical information is assembled through image analysis to yield physical attributes such as document dimensionality, text alignment, and color distribution. The resulting lexical and physical information is combined into an input vector X and is used to train a supervised neural network to perform the classification.