Common Law Admission Test

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Hi,

Warm Greetings!

We hope this email finds you well! We take immense pleasure in introducing
LexHawk Consulting Private Limited, a Legal Process Outsourcing
(LPO) Company based in India.

LexHawk provides world-class legal support for international and domestic
Corporations and Law Firms. LexHawk enables our clients to manage
ever-increasing legal costs without compromising on quality by bringing
together a dedicated management team and highly trained Indian Attorneys.

Our Support Solutions include:

•    Contract Management/Lease Abstraction
•    Legal AI/ML Support Services
•    Patent Search
•    Litigation Support Services
•    Legal Research
•    Judgment Head Notes
•    Trial Transcript Summary Writing
•    Tax forms filling
•    Loans re-rewriting
•    UK mortgaging work

LexHawk’s focus is to provide high Quality, Cost-Effective Document Review
and Management Services for Global Corporations, and Law Firms. Our
India-based Attorneys and Technical Experts are trained to analyze, code,
and to abstract materials for document review and to provide other
essential legal support services. We meet client-specified requirements in
an efficient, skillful manner, and we transfer completed documents to our
clients electronically and securely. LexHawk has a relentless commitment
to quality and timeliness.

LexHawk is open to working on any new assignments and would like to explore
areas of cooperation with your esteemed organization. LexHawk could
initiate projects in the shortest Turn-around-Time and are open to any
pilot/sample projects to prove our capabilities. We hope you would give us
an opportunity to serve your organization/law firm.

For further information, please visit our website which will provide a
comprehensive idea about LexHawk at: http//www.lexhawk.com. consulting@lexhawk.com

 

Head-note writing

 https://www.google.com/search?q=headnote+writing&oq=head&aqs=chrome.1.69i57j69i59j0i67i457j0i67j0i67i433j0i433j0i67j0i433.3642j0j7&sourceid=chrome&ie=UTF-8

An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool

 https://github.com/robertarvind/Interactive-Semi-Automatic-Image-2D-Bounding-Box-Annotation-Tool-using-Multi-Template_Matching

An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool to aid the Annotater/User to rapidly create 2D Bounding Box Single Object Detection masks for large number of training images in a semi automatic manner in order to train an object detection deep neural network such as Mask R-CNN or U-Net. As the Annotater/User starts annotating/labelling by drawing a bounding box for a few number of images in the selected folder then the algorithm suggests bounding box predictions for the rest of the yet to be annotated/labelled images in the folder. If the predictions are right then the user/annotater can simply press the keyboard key 'y' which indicates that the detected bounding box is correct. If the prediction is wrong then the user/annotater can manually draw a rectangular 2D bounding box over the correct ROI (Region of interest) in the image and then press the key 'y' to proceed further to the rest of the images in the folder. If the user/annotater made a mistake while drawing the 2D bounding box, then he/she can press the key 'n' in order to remove the incorrectly marked 2D bounding box and he/she can repeat the process for the same image until he/she draws the correct 2D bounding box and then after drawing the correct 2D bounding box, the user/annotater may press the key 'y' to continue to the rest of the images. The 2D bounding box prediction over the whole image data set improves as the user/annotater annotates/labels more number of images by drawing 2D bounding boxes. This tool allows the user/annotater to not only interactively and rapidly annotate large number of images but also to validate the predictions at the same time interactively. This tool helps the user/annotater to save a lot of time when annotating/labelling and validating the predictions for a large number of training images in a folder.