Tata Consultancy Services Limited

Prosenjit Mondal

Devang Jagdishchandra Patel

Amit Kumar Agrawal

Pushp Kumar Jain

Prabhat Ranjan Mishra

Rajdeep Chatterjee

Mantu Prasad Gupta

Sumit Raut

Ankur Chakraborty

Ketkee Pandit

Ankita Gupta

Dinesh Yadav

Tanmay Suhas Jagtap

Harinakshi Raina

Mohit Pandey

Abhishek Rajpoot

Anshika Jain

Pushpkumar Jain

ROI (Region of Interest) detection is an important step in extracting relevant information from a document image. Such images are very high-resolution images in nature and size of images is in order of megabytes, which makes text detection pipeline very slow. Traditional methods detect and extract ROI from images, but these work only for specific image types. Other approaches include deep learning (DL) based methods for ROI detect which need intensive training and require high end computing infrastructure/resources with graphical processing unit (GPU) capabilities. Systems and methods of the present disclosure perform ROI extraction by partitioning input image into parts based on its visual perception and then classify the image in first or second category. Region of interest is extracted from a resized image based on the classification by applying image processing techniques. Further, the system determines whether the input image is a pre-cropped image or a normal scanned image.

Extracting region of interest from scanned images and determining an associated image type thereof

Variation in received documents types and templates used for each document type poses challenge in developing a generic background noise removal approach for automatic text information extraction technique. Embodiments herein provide a method and a system for document classification and text information extraction. Time efficient and accurate text detection engine-based Region of Interest (ROI) technique is provided to accurately identify text region followed by a multi-layered neural network based architecture for enhanced classification accuracy to identify the type of document. A multistage image pre-processing approach is provided for efficient, effective, and accurate background noise removal from the classified document, which includes unsupervised clustering, identification, segmentation, masking, contour approximation, selective subtraction, and dynamic thresholding. Further, text information extraction is applied on the pre-processed image to determine co-relation between text information blocks and associate values with the labels for attributes to be extracted to extract final text.

Method and system for document classification and text information extraction

This disclosure relates generally to doorstep delivery of services and products, and more particularly to a system and a method for dynamic fleet management for order delivery are provided. Initially, a primary route is assigned to a vehicle from a fleet of vehicles, based on at least one of a known order and a forecasted order. Further, when the vehicle is in transit along the primary route, in response to an input with respect to at least one of a route alteration parameter, one of an alternate route or a corrective action, is determined, and one or more corresponding actions are triggered, which helps with the order delivery.

System and method for dynamic fleet management

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Kolkata, India

Prosenjit Mondal