Tata Consultancy Services Limited

Devang Jagdishchandra Patel

Amit Kumar Agrawal

Pushp Kumar Jain

Prabhat Ranjan Mishra

Prosenjit Mondal

Mantu Prasad Gupta

Rajdeep Chatterjee

Dinesh Yadav

Ankita Gupta

Ketkee Pandit

Chirabrata Bhaumik

Ankur Chakraborty

Sumit Raut

Pushpkumar Jain

Tanmay Suhas Jagtap

Harinakshi Raina

Anshika Jain

Mohit Pandey

Abhishek Rajpoot

This disclosure relates generally to method and system for detecting and extracting price region from digital flyers and promotions. In retail business, extracting price information from digital flyers is crucial for complex nature of flyers having large variety of formats, color scheme, font styles, variable text information and thereof. The method of the present disclosure detects a text region comprising a price information from a set of digital flyers and promotions received as input images. Further, each text region is converted into a two-color text comprising of a set of white pixels and a set of black pixels. Further, underlying price from the price region of the two-color text is detected and price is extracted from the price region of each input image. Additionally, the price region detection function detects price region accurately and extracts price values having an irregular font size.

Method and system for detecting and extracting price region from digital flyers and promotions

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

This disclosure relates generally to method and system for determining label value for labels in unstructured documents. Typical systems have challenge in understanding variations in layout of unstructured documents and extract information therefrom. The disclosed method and system facilitate systematically identifying sections and bounding boxes in the page images, taking image portion of the bounding boxes and extracting labels and label values therefrom. In case the label values are not present in the same bounding box having the label, the neighboring labels are examined for the matching label values. The system also obtains label-label value pairs from the document by utilizing a trained deep learning model, and compares the output with the label-label value pairs extracted earlier. An aggregated confidence score is assigned to the text in the bounding box.

System and method for determination of label values in unstructured documents

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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Mumbai, India

Devang Jagdishchandra Patel