Accenture Global Solutions Limited

Rahul Kotnala

Prakash Ghatage

Kumar Viswanathan

Nirav Sampat

Naveen Kumar Thangaraj

Ashish Jain

Ramakrishnan Raman

Sattish Sundarakrishnan

Srinivasan Krishnan Rajagopalan

Manikandan Chandran

Anand Yesuraj Prakash

Earnest Paul Wesley

Prakash Patil

Rekha Mani

Aravind Krishnan

Shishir Kumar Roy

Yakob Raja R

Kameshkumar Lakshminarayanan

Simantini Deb

Vijay Dhandapani

Peter Ashly Gopalan

Loganathan Muthu

Suvendu Kumar Mahapatra

RBSanthosh Kumar

Harbhajan Singh

Kavitha Subramanian

Lokesh Venkatappa

Srikanth Narayanan

Indrajeet

A system and method for automating and improving data extraction from a variety of document types, including both unstructured, structured, and nested content, is disclosed. The system and method incorporate an intelligent machine learning model that is designed to intelligently identify chunks of text, map the fields in the document, and extract multi-record values. The system is designed to operate with little to no human intervention, while offering significant gains in accuracy, data visualization, and efficiency. The architecture applies customized techniques including density-based adaptive text clustering, tabular data extraction based on hierarchical intelligent keyword searches, and natural language processing-based field value selection.

Intelligent data extraction system and method

An Artificial Intelligence (AI)-based data transformation system receives an input package and enables automatic execution of one or more processes in a robotic process automation system (RPA). The input package includes a plurality of documents and metadata required for the execution of the automated processes. The plurality of documents are categorized into a domain. Entities with their corresponding name-value pairs and entity relationships are extracted from the plurality of documents. An ontology is selected based on the domain. The entities are mapped to output fields identified from the selected ontology. The mappings thus generated are transmitted to the RPA system which employs the mappings to automatically execute the one or more processes.

Data transformations for robotic process automation

Data extraction and automatic validation from digitized documents in non-editable formats is disclosed. Paper documents are digitized or converted into formats suitable for storage on computers or other digital devices. The digitized documents are classified into one of a plurality of document types and based on the document type, document processing rules are selected for analyzing the digitized documents to enable data extraction and automatic validation. The positions and values of the data fields in the digitized documents are obtained using machine learning techniques. The data field values are automatically validated and assigned confidence scores. Data fields with low confidence scores are flagged for manual review.

Extracting searchable information from a digitized document

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Dehradun, India

Rahul Kotnala