Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure relates generally to document processing, and more particularly to method and system for determining structural blocks of a document. In one embodiment, the method may include extracting text from the document, the text including text lines. The method may further include generating a feature vector for each of the text lines, the feature vector for the text line including a set of feature values for a set of corresponding features in the text line. The method may further include creating an input matrix for each of the text lines, the input matrix for the text line including a set of feature vectors corresponding to a set of neighboring text lines along with the text line. The method may further include determining a structural block tag for each of the text lines based on the corresponding input matrix using a machine learning model.

Abstract: 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.

Abstract: This disclosure relates generally to document processing, and more particularly to method and system for determining structural blocks of a document. In one embodiment, the method may include extracting text from the document, the text including text lines. The method may further include generating a feature vector for each of the text lines, the feature vector for the text line including a set of feature values for a set of corresponding features in the text line. The method may further include creating an input matrix for each of the text lines, the input matrix for the text line including a set of feature vectors corresponding to a set of neighboring text lines along with the text line. The method may further include determining a structural block tag for each of the text lines based on the corresponding input matrix using a machine learning model.