Abstract: A method and system of comparing at least two digital labels is disclosed. A processor detects a set of regions of interest (ROIs) corresponding to a set of objects in each of the at least two digital labels using a first Machine Learning (ML)/Deep Learning (DL) model. Each of the set of objects are classified into one of a set of text objects or a set of non-text objects. One or more key-value pairs of one or more attributes are extracted from the text object. The one or more key-value pairs text data are compared corresponding to the text object of the at least two digital labels. The each of non-text object of the set of non-text objects are categorized into an object category from a set of object categories. An ROI of the each of non-text object of the set of non-text objects may be compared.

Abstract: A system and method of extracting tables and figures from a drawing document is disclosed. The method may include processing coloured image to segmented binary image and extracting a plurality of horizontal lines and a plurality of vertical lines from a foreground of the image. The method may further include detecting a set of candidate table region from the plurality of horizontal lines and the plurality of vertical lines in the image. Further, the method may include calculating textual region density corresponding to each of the set of candidate table regions in the image. The method may further include identifying at least one relevant table region from the set of candidate table regions in the image and a text free region from the at least one additional region in the image. The method may further include identifying at least one figure region from the dilated text free region.

Abstract: A method of extracting dimension data from a document is disclosed. The method includes receiving the document comprising at least one two-dimensional figure and a plurality of dimension sets associated with the at least one two-dimensional figure. The method may include detecting the at least one two-dimensional figure in the document. The method may further include detecting the plurality of dimension sets distinctly from the at least one two-dimensional figure in the document. Further, the method may identify a plurality of arrowheads associated with the plurality of dimension sets. The method may include clustering the plurality of arrowheads to obtain a plurality of set of arrowheads. The method may further include mapping each of the plurality of set of arrowheads with the dimension value and extracting dimension data corresponding to each of the plurality of set of arrowheads based on the mapping.

Abstract: A method and system of extracting borderless structure using image processing is disclosed. The method may include converting a received document into a binary image comprising a plurality of text characters. A first image is created comprising a plurality of text blobs by connecting text characters and merging the plurality of text blobs to create one or more text line blobs to generate a second image. Further the first image and the second image are compared to generate a third image comprising a plurality of gap blobs. The gap blobs are clustered into one or more groups to determine a localized region of interest (ROI). Further lines are identified within the ROI using pixel density and separated into rows and columns. The final output contains list of cell coordinates.

Abstract: A method and system for generating binary image of a document image is disclosed. The method includes determining a negative map image, an inverse negative map image, an HSV image and a grayscale image of the document image. One or more cells corresponding to at least one table are detected based on detection of lines in the negative map image. For each of the cells, a foreground mean value, a background mean value, and a background mean HSV value is determined. Each of the cells are categorized as a dark cell or a light cell based on the foreground mean value and the background mean value. Contrast value of each cell is determined based on the foreground mean value and the background mean value. The binary image is determined based on the contrast value, a pre-defined threshold value, the background mean HSV value and the categorization of the corresponding cell.

Abstract: A method and system of determining shape of a table in a document is disclosed. A region of interest (ROI) from a binarized image of the document is determined is detected corresponding to the table based on detection of a plurality of lines. The ROI is extracted based on a minimum height threshold and a minimum width threshold of the document image. A cluster of points corresponding to each corner of the ROI are determined based on a height of the ROI and contour detection. A corner type of each corner is determined to be one of a 10 pointed corner or a curved corner and in case the corner type of least two corners is determined as the curved corner the shape of the table is determined as a rounded corner structure.

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: 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: 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: 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.