Wipro Limited

Raghavendra Hosabettu

Prashanth Krishnapura Subbaraya

Raghottam Mannopantar

Anil Kumar Lenka

RamPrasad Kanakatte Ramanna

Sneha Subhaschandra Banakar

Harihara Vinayakaram Natarajan

Parthasarathy Shunmugam Arumugasamy

Ponnusamy Ananthasankaranarayanan

Sendil Kumar Jaya Kumar

Raja Sekhar Reddy Sudidhala

Srinivas Adyapak

Abhijith Vijaya Kumar Rugminibai

Nita Aknurwar

Raghav Nagpal

Tarun Mishra

Adithya

Anoop Unnikrishnan

Kiran Kumar Channarayapatna Sathyanarayana

Parthasarathy Shunmugam Armugasamy

This disclosure relates to method and system for optimizing artificial neural network (ANN) classification model and training data thereof for appropriate model behavior. The method may include extracting entities and domain specific entities from the training data for each of classes of the ANN classification mode, determining model parameters of the ANN classification model based on the training data, determining missing data with respect to the training data or the model parameters based on the entities and the domain specific entities for each the classes, iteratively analysing a relative advantage of a modified ANN classification model with a modified training data with respect to the ANN classification model with the training data, and determining an optimized ANN classification model and an optimized training data for appropriate model behavior based on the iterative analysis. The modified data may be generated based on the missing data.

Optimization of artificial neural network (ANN) classification model and training data for appropriate model behavior

The disclosure relates to method and system for improving performance of an artificial neural network (ANN) model. The method includes receiving the ANN model and input dataset. The ANN model includes neurons arranged in multiple layers and employing corresponding activation functions. The method further includes assigning a random activation threshold value to each of the corresponding activation functions, determining activated neurons in each layer for a majority of input data in the input dataset based on the random activation threshold value for each of the corresponding activation functions, identifying removable layers based on a number of activated neurons and a pre-defined threshold value, evaluating a relative loss of the ANN model upon removing each removable layer from the ANN model and for a random input data in the input dataset, and deriving a modified ANN model by removing one or more of the removable layers based on the evaluation.

Method and system for improving performance of an artificial neural network (ANN) model

This disclosure relates to method and system for improving performance of an artificial neural network (ANN). The method may include generating a weight matrix comprising weights of neural nodes in a given layer for each layer of the ANN, determining a marginal contribution value of a given neural node for each neural node in the given layer with respect to other neural nodes in the given layer, executing an elimination decision for each neural node in each layer based on the corresponding marginal contribution value, determining a distributed surplus value of a given remaining neural node in a given layer based on the marginal contribution values of a coalition of remaining neural nodes in the given layer for each remaining neural node in each layer, and updating the weight matrix based on the distributed surplus value of each remaining neural node in each layer.

Method and system for improving performance of an artificial neural network

This disclosure relates to method and system for verifying a positive classification performed by an artificial neural network (ANN) in a given class. The method includes generating a weight matrix comprising a weight of each neural node in a given layer; determining a contribution factor of a given neural node in the given layer with respect to an output of the ANN for the given class based on a known input vector to the given layer and a modified weight matrix; and generating a dominance matrix based on the contribution factor of each neural node in the given layer. The method further includes determining a rank of each neural node based on the corresponding dominance factor; and verifying the positive classification performed by the ANN in the given class for a test input vector based on the rank of each neural node in each layer of the ANN.

Method and system for verifying classification performed by an artificial neural network

This disclosure relates to method and system for improving classifications performed by artificial neural network (ANN) model. The method may include identifying, for a classification performed by the ANN model for an input, activated neurons in each neural layer of the ANN model; and analyzing the activated neurons in each neural layer with respect to Characteristic Feature Directive (CFDs) for corresponding neural layer and for a correct class of the input. The CFDs for each neural layer may be generated after a training phase of the ANN model and based on neurons in corresponding neural layer that may be activated for a training input of the correct class. The method may further include determining differentiating neurons in each neural layer that are not activated as per the CFDs for the correct class of the input based on the analysis; and providing missing features based on the differentiating neurons.

Method and system for improving classifications performed by an artificial neural network (ANN) model

This disclosure relates generally to image processing, and more particularly to method and system for reconstructing an image. In one embodiment, the method includes pre-processing an input image to generate character images corresponding to characters in the input image, determining a local character thickness threshold value for each character image, determining a global character thickness threshold value for the input image based on the local character thickness threshold values for the character images, and reconstructing each character image based on the local character thickness threshold value for each character image and the global character thickness threshold value to generate reconstructed character images. The local character thickness threshold value in a character image may be based on a set of character pixel values in a pre-determined number of segments in the character image. The method further includes re-constructing the input image based on the reconstructed character images.

System and method for reconstructing an image

Methods, devices, and non-transitory computer readable media that add interoperable BOTs to a stored BOT inventory. Each of the BOTs is associated with a BOT type, has a common structure, and automates at least a portion of an enterprise process. Assembly rules are obtained. Each of the assembly rules includes one or more constraints for inclusion of one or more of the BOTs in one or more of a plurality of workflows based on the one of the BOT types associated with each of the one or more of the BOTs. The workflows are assembled based on one or more the assembly rules. Each of the workflows comprises a subset of the BOTs and each BOT of the subsets of the BOTs is included in one or more of the workflows based on at least one of an associated functionality, performance, or service level.

Methods for creating automated dynamic workflows of interoperable bots and devices thereof

This disclosure relates to method and system for improving performance of an artificial neural network (ANN). The method may include receiving a weight matrix comprising an original weight of each neural node in each layer of the ANN. For each unique combination of at least two neural nodes in each layer, the method may further include determining a relative advantage value for one of the at least two neural nodes in a given layer with respect to remaining of the at least two neural nodes in the given layer based on actual inputs and standard inputs to the at least two neural nodes, and determining a modified weight of each of the at least two neural nodes based on the relative advantage value. The method may further include executing an elimination decision for each neural node in each layer based on a corresponding final modified weight, and updating the weight matrix based on the final modified weight of each remaining neural node in each layer. The final modified weight of a given neural node in a given layer is the modified weight of the given neural node upon exhausting each unique combination of the at least two neural nodes in the given layer.

The present subject matter is related in general to the field of image processing, disclosing method and system for evaluating an image quality for Optical Character Recognition (OCR) Image evaluation system receives image comprising optical character data. The image evaluation system determines image parameter value for each of one or more image parameters of the image. The image parameter value for each of the one or more image parameters is determined for plurality of binary image segments identified in the image. The image evaluation system determines suitability value and impact value of the image, based on the image parameter value for each of the image parameters determined for the image. The image evaluation system determines quality score for the image, based on the suitability value and the impact value. The image is transmitted for processing before the OCR, upon determining the quality score to be above overall pre-defined threshold value.

Method and system for evaluating an image quality for optical character recognition (OCR)

Disclosed herein is a method and device for identifying bold text in a digital document. The system receives image of digital document which comprises text. The system applies bounding box for each text in the image and scans predefined number of lines in each bounding box to identify width values of pixels. Thereafter, system identifies most occurring width value of pixels among the width values of pixels in each bounding box. The most occurring width value of pixels in each bounding box is identified as box width of corresponding bounding box. The system compares box width of each bounding box with threshold box width. If box width is greater than threshold box width, system identifies text of the bounding box whose box width exceeds threshold box width as bold text. The present disclosure efficiently identifies bold text in digital document based on width values of pixels with less computational power.

Method and system for identifying bold text in a digital document

In one embodiment, a system, comprising a processor and a memory, for alerting a user, is disclosed. The processor receives one or more sensor data. The processor further determines one or more environmental factors, a current activity of the user, one or more motion data and one or more objects in proximity to the user based on the one or more sensor data. The processor further generates an initial degree of safety for each of the one or more environmental factors, the current activity of the user, the one or more motion data of the electronic device and the one or more objects in proximity to the user based on dynamically adaptive thresholds. The processor further generates an aggregate degree of safety based on the initial degree of safety and one or more pre-defined rules. The processor further alerts the user based on the aggregate degree of safety.

System and method for alerting a user of risks

A method and device for automatic data correction using context and semantic aware learning techniques is disclosed. The method includes extracting data within a document as machine readable text in a predefined format. The method further includes encoding each word of each line in the machine readable text to a multi-dimension word vector. The method includes generating a context word vector for each word in each line based on multi-dimension vectors associated with words succeeding and preceding the word in a line comprising the word. The method further includes decoding the context word vector associated with each word in each line to generate a corrected context word vector for each word. The method includes validating the corrected context word vector associated with each word in each line.

Method and device for automatic data correction using context and semantic aware learning techniques

A technique is provided for customizing a presentation. The technique includes recording multimedia corresponding to a presenter of a presentation. The recorded multimedia is analyzed to extract a representative information corresponding to the multimedia. Further, one or more pre-recorded multimedia files are determined from a multimedia database. The determination is based on a comparison of the representative information with one or more tags associated with each of a plurality of pre-recorded multimedia files. Subsequently, the presentation is customized by inserting the one or more pre-recorded multimedia in the presentation.

System and method for customizing a presentation

This disclosure relates generally to document processing, and more particularly to method and system for detecting and extracting tabular data from a document. In one embodiment, the method may include generating a hierarchy of features, for a plurality of features of an image document derived from the document, based on relative spatial properties of the plurality of features. The method may further include segmenting the image document into a plurality of semantic segments based on the hierarchy of features, classifying each of the plurality of semantic segments into at least one of a plurality of tabular structures, and effecting at least one of a detection or an extraction of the tabular data from the image document based on the classification.

Method and system for detecting and extracting a tabular data from a document

This disclosure relates to method and system for determining structural blocks of a document. The method may include extracting text lines from the document, generating a feature vector for each text line by determining feature values for a set of features in the each text line, and determining at least one dominant feature from among the set of features and at least one corresponding dominance factor, for each structural class, based on the feature vector for each text line. The method may further include deriving a set of rules for classification of the text lines into respective structural classes and determining a structural block tag for each text line based on the set of rules. Each of the set of rules correspond to one of the structural classes and is based on the at least one dominant feature and the at least one corresponding dominance factor for that class.

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Bengaluru, India

Raghavendra Hosabettu