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1.发明申请公开(公告)号：US20200175372A1
公开(公告)日：2020-06-04
申请号：US16381316
申请日：2019-04-11
Applicant: Tata Consultancy Services Limited
Inventor： Monika SHARMA , Rohit RAHUL , Lovekesh VIG , Shubham PALIWAL
IPC: G06N3/08 , G06N3/04 , G06F16/901 , G06F16/9035
Abstract: 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.
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公开(公告)号：US20190026604A1
公开(公告)日：2019-01-24
申请号：US15895429
申请日：2018-02-13
Applicant: Tata Consultancy Services Limited
Inventor： Monika SHARMA , Lovekesh VIG , Shirish Subhash KARANDE , Anand SRIRAMAN , Ramya Sugnana Murthy HEBBALAGUPPE
Abstract: The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end-to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN).
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公开(公告)号：US20200012838A1
公开(公告)日：2020-01-09
申请号：US16246278
申请日：2019-01-11
Applicant: Tata Consultancy Services Limited
Inventor： Monika SHARMA , Swati JINDAL , Lovekesh VIG
Abstract: Method and system for automatic chromosome classification is disclosed. The system, alternatively referred as a Residual Convolutional Recurrent Attention Neural Network (Res-CRANN), utilizes property of band sequence of chromosome bands for chromosome classification. The Res-CRANN is end-to-end trainable system, in which a sequence of feature vectors are extracted from the feature maps produced by convolutional layers of a Residual neural networks (ResNet), wherein the feature vectors correspond to visual features representing chromosome bands in an chromosome image. The sequence feature vectors are fed into Recurrent Neural Networks (RNN) augmented with an attention mechanism. The RNN learns the sequence of feature vectors and the attention module concentrates on a plurality of Regions-of-interest (ROIs) of the sequence of feature vectors, wherein the ROIs are specific to a class label of chromosomes. The Res-CRANN provides higher classification accuracy as compared to the state-of the-art methods for chromosome classification.
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Patent Agency Ranking
公开(公告)号：US20180173988A1
公开(公告)日：2018-06-21
申请号：US15839004
申请日：2017-12-12
Applicant: Tata Consultancy Services Limited
Inventor： Monika SHARMA , Lovekesh Vig , Ramya Surnana Murthy Hebbalaguppe , Ehtesham Hassan , Ankit Verma
Abstract: This disclosure relates generally to character detection and recognition, and more particularly to a method and system for container code recognition via Spatial Transformer Networks and Connected Component. The method comprises capturing an image of a container using an image capture device which is pre-processed using an image preprocessing module. The method further comprises extracting and filtering region proposals from the pre-processed image using a region extraction module to generate regrouped region proposals. The next step comprises classifying the regrouped region proposals into characters by implementing trained Spatial Transformation Network to generate a valid group of region proposal with more than one chunk of container identification code using a classification module, and lastly a sequence for the valid group of region proposal is generated and the generated sequence is mapped to a predefined standard container identification code to determine a container identification code, wherein the predefined standard identification code comprises chunks of characters in a predefined pattern.
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公开(公告)号：US20230045646A1
公开(公告)日：2023-02-09
申请号：US17722527
申请日：2022-04-18
Applicant: Tata Consultancy Services Limited
Inventor： Shubham Singh PALIWAL , Lovekesh VIG , Monika SHARMA
Abstract: Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.
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公开(公告)号：US20220215683A1
公开(公告)日：2022-07-07
申请号：US17607437
申请日：2020-09-06
Applicant: Tata Consultancy Services Limited
Inventor： Kushagra MAHAJAN , Monika SHARMA , Lovekesh VIG
Abstract: Keypoint extraction is done for extracting keypoints from images of documents. Based on different keypoint extraction approaches used by existing keypoint extraction mechanisms, number of keypoints extracted and related parameters vary. Disclosed herein is a method and system for keypoint extraction from images of one or more documents. In this method, a reference image and a test image of a document are collected as input. During the keypoint extraction, based on types of characters present in words extracted from the document images, a plurality of words are extracted. Further, all connected components in each of the extracted words are identified. Further, it is decided whether keypoints are to be searched in a first component or in a last component of all the identified connected components, and accordingly searches and extracts at least four of the keypoints from the test image and the corresponding four keypoints from the reference image.
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7.发明申请公开(公告)号：US20200175304A1
公开(公告)日：2020-06-04
申请号：US16353570
申请日：2019-03-14
Applicant: Tata Consultancy Services Limited
Inventor： Lovekesh VIG , Gautam SHROFF , Arindam CHOWDHURY , Rohit RAHUL , Gunjan SEHGAL , Vishwanath DORESWAMY , Monika SHARMA , Ashwin SRINIVASAN
IPC: G06K9/46 , G06F17/27 , G06T5/00 , G06K9/00 , G06K9/62 , G06F16/2452 , G06F16/2455 , G06F16/28 , G06N3/08
Abstract: 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.
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8.发明申请公开(公告)号：US20160284100A1
公开(公告)日：2016-09-29
申请号：US15019794
申请日：2016-02-09
Applicant: Tata Consultancy Services Limited
Inventor： Monika SHARMA , Hiranmay GHOSH
CPC classification number: G06T7/40 , G06K9/4642 , G06K9/6212 , G06T5/002 , G06T5/003 , G06T5/40 , G06T7/11 , G06T7/44 , G06T2207/10032 , G06T2207/20021 , G06T2207/30188
Abstract: This disclosure relates generally to image processing, and more particularly to image texture determination. In one embodiment a processor a memory coupled to the processor, wherein the processor coupled with a plurality of modules stored in the memory: At least one image having a plurality of pixels is accepted. Any noise is removed from the image to obtain at least one noise free image. The at least one noise free image is converted to at least one gray scale image. Horizontal and Vertical Gradient for plurality of pixels of the at least one gray scale image are computed. Gradient magnitude is calculated for the generated gradient. Histogram of the gradient magnitude is generated based on the gradient magnitude, and the plurality of generated histograms are compared with a plurality of predetermined histograms.
Abstract translation: 本公开一般涉及图像处理，更具体地涉及图像纹理确定。 在一个实施例中，处理器耦合到所述处理器，其中所述处理器与存储在所述存储器中的多个模块耦合：至少一个具有多个像素的图像被接受。 从图像中去除任何噪声，以获得至少一个无噪声图像。 至少一个无噪声图像被转换成至少一个灰度图像。 计算用于至少一个灰度图像的多个像素的水平和垂直渐变。 对于生成的梯度计算梯度大小。 基于梯度大小生成梯度幅度的直方图，并将多个生成的直方图与多个预定直方图进行比较。
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