Tata Consultancy Services Limited

Rohit Rahul

Lovekesh Vig

Monika Sharma

Shubham Paliwal

Arindam Chowdhury

Gautam Shroff

Ashwin Srinivasan

Gunjan Sehgal

Animesh

Samarth Mittal

Vishwanath Doreswamy Gowda

Vishwanath Doreswamy

Animesh,

The need for extracting information trapped in unstructured document images is becoming more acute. A major hurdle to this objective is that these images often contain information in the form of tables and extracting data from tabular sub-images presents a unique set of challenges. Embodiments of the present disclosure provide systems and methods that implement a deep learning network for both table detection and structure recognition, wherein interdependence between table detection and table structure recognition are exploited to segment out the table and column regions. This is followed by semantic rule-based row extraction from the identified tabular sub-regions.

Deep learning based table detection and associated data extraction from scanned image documents

Systems and methods for automating information extraction from piping and instrumentation diagrams is provided. Traditional systems and methods do not provide for end-to-end and automated data extraction from the piping and instrumentation diagrams. The method disclosed provides for automatic generation of end-to-end information from piping and instrumentation diagrams by detecting, via one or more hardware processors, a plurality of components from one or more piping and instrumentation diagrams by implementing one or more image processing and deep learning techniques; associating, via an association module, each of the detected plurality of components by implementing a Euclidean Distance technique; and generating, based upon each of the associated plurality of components, a plurality of tree-shaped data structures by implementing a structuring technique, wherein each of the plurality of tree-shaped data structures capture a process flow of pipeline schematics corresponding to the one or more piping and instrumentation diagrams.

Systems and methods for automating information extraction from piping and instrumentation diagrams

This disclosure relates to digitization of industrial inspection sheets. Digital scanning of paper based inspection sheets is a common process in factory settings. The paper based scans have data pertaining to millions of faults detected over several decades of inspection. The technical challenge ranges from image preprocessing and layout analysis to word and graphic item recognition. This disclosure provides a visual pipeline that works in the presence of both static and dynamic background in the scans, variability in machine template diagrams, unstructured shape of graphical objects to be identified and variability in the strokes of handwritten text. The pipeline incorporates a capsule and spatial transformer network based classifier for accurate text reading and a customized Connectionist Text Proposal Network (CTPN) for text detection in addition to hybrid techniques for arrow detection and dialogue cloud removal.

Digitization of industrial inspection sheets by inferring visual relations

Various methods are using SQL based data extraction for extracting relevant information from images. These are rule based methods of generating SQL-Query from NL, if any new English sentences are to be handled then manual intervention is required. Further becomes difficult for non-technical user. A system and method for extracting relevant from the images using a conversational interface and database querying have been provided. The system eliminates noisy effects, identifying the type of documents and detect various entities for diagrams. Further a schema is designed which allows an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added so that a non-technical user, specifying the queries in natural language, can fetch the information effortlessly.

Method and system for information extraction from document images using conversational interface and database querying

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Gurgaon, India

Rohit Rahul