Abstract: Present invention discloses method and system for generating knowledge graph and sub-graph clusters to perform a root cause analysis. Method comprising extracting at least one of objects, data entities, links between the objects and the data entities, or relationships between the objects and the data entities from input content. Thereafter, method comprising generating a knowledge graph from the extracted data and sub-graphs from the knowledge graph using an unsupervised ML technique and extracting graph data structure information for each sub-graph. Subsequently, method comprising generating root cause model based on the sub-graphs and the graph data structure information and generating at least one sub-graph cluster and corresponding probabilistic graphical model using the root cause model and the knowledge graph.

Abstract: The present disclosure discloses method and a road region determination system for determining drivable road regions for safe navigation of an autonomous vehicle. The road region determination system receives real-time images of road in which autonomous vehicle is travelling, from image sensors. Each of real-time images of road is segmented into polygon regions and trend lines to obtain plurality of features using pre-trained road segmentation model. An orientation of the road is identified in real-time images as one of linear orientation and non-linear orientation based on slope measured between successive intermediate points distributed evenly on trend lines. The road region determination system manages redistribution of intermediate points on trend lines based on orientation of the road. Thereafter, paired points from intermediate points redistributed on the trend lines is identified, where paired points are connected using a horizontal line to determine drivable road regions for autonomous vehicle.

Abstract: The present disclosure is related in general to image processing and a method and system for generating a multi-level classifier for image processing. An image processing system may analyze an input image of a predetermined image type to extract unique key feature descriptors associated with the input image. Further, the unique key feature descriptors are resized into a predefined standard template format which is utilized to develop an image type classifier. Furthermore, the unique key feature descriptors are resized into each of one or more template classifiers of the predetermined image type. Further, signal quality value of each of the template classifiers is determined by validating each of the unique key feature descriptors resized based on each of the template classifiers and an image prediction classifier is developed based on the signal quality value.

Abstract: Methods, devices, and non-transitory computer readable storage media for extracting text from documents are disclosed. The method includes performing layout analysis on the document to identify a plurality of regions within a plurality of pages in the document. The method further includes identifying a table region from within the plurality of regions based on homogeneity between a plurality of textual lines in a page from the plurality of pages. The method includes identifying at least two rows and at least two columns within the table region. The method further includes identifying a plurality of cells within the table region based on the at least two rows and the at least two columns. The method includes extracting text from each of the plurality of cells.

Abstract: Methods, devices, and non-transitory computer readable storage media for extracting text from documents are disclosed. The method includes performing layout analysis on the document to identify a plurality of regions within a plurality of pages in the document. The method further includes identifying a table region from within the plurality of regions based on homogeneity between a plurality of textual lines in a page from the plurality of pages. The method includes identifying at least two rows and at least two columns within the table region. The method further includes identifying a plurality of cells within the table region based on the at least two rows and the at least two columns. The method includes extracting text from each of the plurality of cells.

Abstract: The present disclosure is related in general to image processing and a method and system for generating a multi-level classifier for image processing. An image processing system may analyse an input image of a predetermined image type to extract unique key feature descriptors associated with the input image. Further, the unique key feature descriptors are resized into a predefined standard template format which is utilized to develop an image type classifier. Furthermore, the unique key feature descriptors are resized into each of one or more template classifiers of the predetermined image type. Further, signal quality value of each of the template classifiers is determined by validating each of the unique key feature descriptors resized based on each of the template classifiers and an image prediction classifier is developed based on the signal quality value.

Abstract: In one embodiment, an image processing device for detecting tampering in a document image is disclosed. The image processing device comprises a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, which, on execution, causes the processor to determine an image quality of the document image by analyzing one or more quality features extracted from the document image. The processor is caused to pre-process the document image based on a pre-defined ontology of documents when the image quality is above a pre-defined quality threshold. Further, the processor is caused to segment the pre-processed document image into one or more region of interests based on the pre-defined ontology of documents and detect tampering in a region of interest in the document image by processing each region of interest of the one or more region of interests to detect tampering in the document image.

Abstract: The present disclosure relates to a method and system for processing an image extracted from a document. The image processing system detects one or more edges of the image and determines a missing edge in the image by comparing color gradients of the edges. The missing edges are recreated by cloning image pixels based on data pixels of an edge, opposite to the missing edge, amongst the one or more edges to obtain a reconstructed image. Outer corner points in the reconstructed image are identified based on quadratic corner points, wherein the quadratic corner points are determined based on the one or more edges and the reconstructed edge. Further, the image processing system performs an image perspective correction on the reconstructed image, based on the outer corner points, to obtain a processed image.

Abstract: In one embodiment, an image processing device for detecting tampering in a document image is disclosed. The image processing device comprises a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, which, on execution, causes the processor to determine an image quality of the document image by analyzing one or more quality features extracted from the document image. The processor is caused to pre-process the document image based on a pre-defined ontology of documents when the image quality is above a pre-defined quality threshold. Further, the processor is caused to segment the pre-processed document image into one or more region of interests based on the pre-defined ontology of documents and detect tampering in a region of interest in the document image by processing each region of interest of the one or more region of interests to detect tampering in the document image.

Abstract: The present disclosure relates to a method and system for processing an image extracted from a document. The image processing system detects one or more edges of the image and determines a missing edge in the image by comparing color gradients of the edges. The missing edges are recreated by cloning image pixels based on data pixels of an edge, opposite to the missing edge, amongst the one or more edges to obtain a reconstructed image. Outer corner points in the reconstructed image are identified based on quadratic corner points, wherein the quadratic corner points are determined based on the one or more edges and the reconstructed edge. Further, the image processing system performs an image perspective correction on the reconstructed image, based on the outer corner points, to obtain a processed image.