公开(公告)号：US20190272420A1

公开(公告)日：2019-09-05

申请号：US15938806

申请日：2018-03-28

Applicant: Tata Consultancy Services Limited

Inventor： GAURAV GUPTA ,  SWATI ,  MONIKA SHARMA ,  LOVEKESH VIG

IPC: G06K9/00 ,  G06K9/62

Abstract: This disclosure relates generally to document processing, and more particularly to extracting information from hand-marked industrial inspection sheets. In an embodiment, the system performs localization of text as well as arrows in the inspection sheet, and identifies text that matches each arrow. Further by identifying machine zone each arrow is pointing to, the system assigns corresponding text to the appropriate machine zone; thus facilitating digitization of the inspection sheets.

公开(公告)号：US20250166134A1

公开(公告)日：2025-05-22

申请号：US18903256

申请日：2024-10-01

Applicant: Tata Consultancy Services Limited

Inventor： ARUSHI JAIN ,  SHUBHAM SINGH PALIWAL ,  MONIKA SHARMA ,  VIKRAM JAMWAL ,  LOVEKESH VIG

IPC: G06T5/60 ,  G06T5/50 ,  G06T5/70 ,  G06T7/11 ,  G06T7/194 ,  G06T11/60 ,  G06V10/764 ,  G06V10/776

Abstract: Text-to-image models are used to generate images based on text prompts. Existing text-to-image models create images that are often unclear and exhibit hybrid characteristics of multiple subjects i.e., each subject present in image exhibit characteristic of multiple subjects. Present disclosure provides a method and a system for personalized multi-subject text to image generation. The system first fine-tunes existing text-to-image diffusion model using a plurality of images of target subjects. Then, the system performs image generation based on local text prompts using the fine-tuned text-to-image diffusion model. In particular, the fine-tuned text-to-image diffusion model uses a composite diffusion algorithm for generating subject images. Thereafter, the system model computes a subject aware segmentation loss for generated images which is then used to correct the subject appearance in the generated images. Finally, the system applies a global diffuser to the generated images to create a harmonized image based on a global text prompt.

公开(公告)号：US20220319217A1

公开(公告)日：2022-10-06

申请号：US17594250

申请日：2020-03-09

Applicant: Tata Consultancy Services Limited

Inventor： SHUBHAM SINGH PALIWAL ,  VISHWANATH DORESWAMY GOWDA ,  ROHIT RAHUL ,  MONIKA SHARMA ,  LOVEKESH VIG

IPC: G06V30/414 ,  G06V30/18 ,  G06V30/262 ,  G06V10/82

Abstract: 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.

公开(公告)号：US20250131185A1

公开(公告)日：2025-04-24

申请号：US18883765

申请日：2024-09-12

Applicant: Tata Consultancy Services Limited

Inventor： ARUSHI JAIN ,  SHUBHAM SINGH PALIWAL ,  MONIKA SHARMA ,  LOVEKESH VIG ,  GAUTAM SHROFF

IPC: G06F40/174 ,  G06F40/106 ,  G06V30/412

Abstract: Robotic Process Automation (RPA) systems face challenges in handling complex processes and diverse screen layouts that require advanced human-like decision-making capabilities. These systems typically rely on pixel-level encoding through drag-and-drop or automation frameworks such as Selenium to create navigation workflows, rather than visual understanding of screen elements. Present disclosure provides systems and methods that implement large language models (LLMs) coupled with deep learning based image understanding which adapt to new scenarios, including changes in user interface and variations in input data, without the need for human intervention. System of the present disclosure uses computer vision and natural language processing to perceive visible elements on graphical user interface (GUI) and convert them into a textual representation. This information is then utilized by LLMs to generate one or more navigation workflows that include a sequence of actions that are executed by a scripting engine/code to complete an assigned task from a task-request.

公开(公告)号：US20240020834A1

公开(公告)日：2024-01-18

申请号：US18346662

申请日：2023-07-03

Applicant: Tata Consultancy Services Limited

Inventor： MANU SHEORAN ,  MONIKA SHARMA ,  LOVEKESH VIG

IPC: G06T7/00 ,  G06V10/77 ,  G06V10/764 ,  G06V10/778 ,  G06V10/82 ,  G06T7/60 ,  G06T7/73 ,  G06V20/70 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06V10/7715 ,  G06V10/764 ,  G06V10/778 ,  G06V10/82 ,  G06T7/60 ,  G06T7/73 ,  G06V20/70 ,  G16H30/40 ,  G16H50/20 ,  G06V2201/03 ,  G06T2207/30096 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/10072

Abstract: The present disclosure detects lesions in different datasets using a semi-supervised domain adaptation manner with very few labeled target samples. Conventional approaches suffer due to domain-gap between source and target domain. Initially, the system receives an input image, and extracts a plurality of multi-scale feature maps from the input image. Further, a classification map is generated based on the plurality of multi-scale feature maps. Further, a 4D vector corresponding to each of a plurality of foreground pixels is computed. Further, an objectness score corresponding the plurality of foreground pixels is computed. After computing the objectness score, a centerness score is computed for each of the plurality of foreground pixels using a single centerness network. Further, an updated objectness score is computed for each of the plurality of foreground. Finally, a plurality of multi-sized lesions in the input image are detected using a trained few-shot adversarial lesion detector network.

公开(公告)号：US20230177678A1

公开(公告)日：2023-06-08

申请号：US17806402

申请日：2022-06-10

Applicant: Tata Consultancy Services Limited

Inventor： MANU SHEORAN ,  MEGHAL DANI ,  MONIKA SHARMA ,  LOVEKESH VIG

IPC: G06T7/00 ,  G06V10/25 ,  G06V10/26 ,  G06V10/778 ,  G06V10/44

CPC classification number: G06T7/0012 ,  G06V10/25 ,  G06V10/26 ,  G06V10/778 ,  G06V10/454 ,  G06T2207/10081 ,  G06T2207/30096 ,  G06V2201/07 ,  G06V2201/032

Abstract: State of the art deep network based Universal Lesion Detection (ULD) techniques inherently depend on large number of datasets for training the systems. Moreover, these system are specifically trained for lesion detection in organs of a Region of interest (RoI) of a body. Thus, requires retraining when the RoI varies. Embodiments herein disclose a method and system for domain knowledge augmented multi-head attention based robust universal lesion detection. The method utilizes minimal number of Computer Tomography (CT) scan slices to extract maximum information using organ agnostic HU windows and a convolution augmented attention module for a computationally efficient ULD with enhanced prediction performance.

公开(公告)号：US20230055391A1

公开(公告)日：2023-02-23

申请号：US17807215

申请日：2022-06-16

Applicant: Tata Consultancy Services Limited

Inventor： ARUSHI JAIN ,  SHUBHAM PALIWAL ,  MONIKA SHARMA ,  LOVEKESH VIG

IPC: G06F16/35 ,  G06F16/335 ,  G06F16/901 ,  G06K9/62

Abstract: State of art techniques that utilize spatial association based Table structure Recognition (TSR) have limitation in selecting minimal but most informative word pairs to generate digital table representation. Embodiments herein provide a method and system for TSR from an table image via deep spatial association of words using optimal number of word pairs, analyzed by a single classifier to determine word association. The optimal number of word pairs are identified by utilizing immediate left neighbors and immediate top neighbors approach followed redundant word pair elimination, thus enabling accurate capture of structural feature of even complex table images via minimal word pairs. The reduced number of word pairs in combination with the single classifier trained to determine the word associations into classes comprising as same cell, same row, same column and unrelated, provides TSR pipeline with reduced computational complexity, consuming less resources still generating more accurate digital representation of complex tables.

公开(公告)号：US20220222956A1

公开(公告)日：2022-07-14

申请号：US17594578

申请日：2020-05-28

Applicant: Tata Consultancy Services Limited

Inventor： MONIKA SHARMA ,  ARINDAM CHOWDHURY ,  LOVEKESH VIG ,  SHIKHA GUPTA

IPC: G06V30/41 ,  G06F16/242 ,  G06N3/04 ,  G06V10/44

Abstract: This disclosure relates generally to intelligent visual reasoning over graphical illustrations using a MAC unit. Prior arts use visual attention to map particular words in a question to specific areas in an image to memorize the corresponding answers, thereby resulting in a limited capability to answer questions of a specific type. The present disclosure incorporates the MAC unit to enable reasoning capabilities and accordingly attend to an area in the image to find the answer. The present disclosure therefore allows generalizing over a possible set of questions with varying complexities so that an unseen question can also be answered correctly based on the reasoning methods that it has learned. The system and method of the present disclosure can be used for understanding of visual information when processing documents like business reports, research papers, consensus reports etc. containing charts and reduce the time spent in manual analysis.