Abstract: The present disclosure recites a system and a method to evaluate the scanned images of tables, identify at least one of the noises, errors and incomplete contours present therein (that make it difficult to extract cell location and it's contains) and process the said images of tables to remove the detected noises/errors from the images of the table and provide final images of table with complete contours. Therefore, said system disclosed herein is configured to take an image of table with incomplete contours (i.e. containing line gaps), as input and provides output in form of a processed image with all contours completed.

Abstract: A method and system of determining quality of a document image is disclosed that includes segmenting, by one or more processors, a document image into a plurality of regions each of which comprises text data. The plurality of regions is classified into one of a plurality of image quality classes based on a determination of a highest prediction value from one of a plurality of machine learning models. The plurality of machine learning models is trained corresponding to one of the plurality of image quality classes. A cumulative quality score for the image is computed based on a weighted average of a number of regions classified into each of the plurality of image quality classes. The quality of the image is determined based on the cumulative quality score.

Abstract: This disclosure relates to method and system for detecting orientation. The method includes detecting a plurality of regions in a document image, each region including text data, and determining positional information of each of the regions; for each of the plurality of regions, determining a region orientation to be one of first orientation or second orientation based on height and width of the region; determining a ratio of number of regions having first orientation and number of regions having second orientation; determining page orientation of the image as third orientation or second orientation, or rotating the image by 90° in counter-clockwise direction based on the ratio; determining first optical character recognition (OCR) data and second OCR data corresponding to the image and the image rotated by 180°, respectively; and determining number of correct words in first OCR data and second OCR data based on comparison with dictionary data.

Abstract: The present invention relates to a compound exhibiting Giant Magneto-Impedance (GMI) properties. The general chemical formula of the compound is (FeXCo100-X)100-(?+?+?)Cr?Si?B?, characterized in that ?<? and ?<?, wherein ? is preferably in the range of 2 to 4% by weight, ? is preferably in the range of 11.5% to 13% by weight, and ? is preferably in the range of 11% to 13% by weight, and X is preferably about 6% by weight. The chemical formula more preferably is (Fe6%Co94%)72.5%Cr2% Si12.5%B13%. The present invention also relates to a giant magneto-impedance (GMI) based sensing device for non-destructive contactless detection of carburization in austenitic stainless steel samples in field.

Abstract: The present invention relates to a compound exhibiting Giant Magneto-Impedance (GMI) properties. The general chemical formula of the compound is (FeXCo100-X)100-(?+?+?)Cr?Si?B?, characterized in that ?<? and ?<?, wherein ? is preferably in the range of 2 to 4% by weight, ? is preferably in the range of 11.5% to 13% by weight, and ? is preferably in the range of 11% to 13% by weight, and X is preferably about 6% by weight. The chemical formula more preferably is (Fe6%Co94%)72.5%Cr2% Si12.5%B13%. The present invention also relates to a giant magneto-impedance (GMI) based sensing device for non-destructive contactless detection of carburization in austenitic stainless steel samples in field.