L&t Technology Services Limited

Niveditha Sureshbabu

Madhusudan Singh

Rajesh Raj

Kalakonda Krishna Vamshi

A method and system of extracting data from a set of documents is disclosed. A processor determines a plurality of spatial features for each of the set of documents based on a set of keywords and a set of entities extracted from the documents. A variance is determined between at least one of the plurality of spatial features determined for each of the documents. Layout is determined based on the spatial features and variance. Each set of documents is clustered in at least one cluster based on a similarity between the layouts of the documents using a first machine learning model. Spatial features are selected using a second machine learning model. Data of the set of entities is extracted corresponding to the keywords in the documents based on the selection of the features and the similarity between the layouts using a third machine learning model.

Method and system of extracting data based on spatial features

A method and system of classifying documents is disclosed. The method includes determining line-text data for each of a plurality of lines of the document image using a text extraction technique. A set of unique keywords in the document image is determined based on a predefined list of keywords in the line-text data for each of the plurality of lines. For each keyword, two forward nodes are determined as next two subsequent keywords in the set of unique keywords based on determination of a shortest distance between the corresponding keyword and the next two subsequent keywords and based on the pre-defined reading sequence. Weights of each of the two forward nodes are determined based on the shortest distance and an angle of each of the two forward nodes with the corresponding keyword. A cluster is determined based on the feature matrix using a machine learning clustering model.

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Bengaluru, India

Niveditha Sureshbabu