Henry Allen Jovero

Key Skills

Software

AWS SageMaker

Appen

Clickworker

CrowdFlower

CVAT

Figure Eight

Humanatic

Labelbox

Label Studio

Lionbridge

OneForma

Prodigy

Remotasks

Scale AI

SuperAnnotate

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Label Types

Bounding Box

Classification

Entity Ner Classification

Evaluation Rating

Segmentation

Freelancer Overview

With over five years of specialized experience, I excel in creating high-quality training data for complex computer vision and natural language processing models. My expertise spans key annotation techniques critical for autonomous systems, including 2D/3D bounding boxes, semantic segmentation, polyline annotation for lane detection, and text annotation for NLP like Named Entity Recognition (NER) and sentiment analysis. I am proficient with industry-standard tools such as Labelbox, CVAT, and Supervisely.

Labeling Experience

Named Entity Recognition for Legal Document Analysis

ProdigyTextEntity Ner Classification

This project involved the creation of a high-quality, specialized dataset to train a Natural Language Processing (NLP) model to automatically extract key entities from complex legal contracts. The goal was to automate the initial review process, saving legal professionals hundreds of hours by instantly identifying parties, dates, obligations, and monetary values within large document volumes.

This project involved the creation of a high-quality, specialized dataset to train a Natural Language Processing (NLP) model to automatically extract key entities from complex legal contracts. The goal was to automate the initial review process, saving legal professionals hundreds of hours by instantly identifying parties, dates, obligations, and monetary values within large document volumes.

2022 - 2025

Drivable Surface and Obstacle Detection for Autonomous Vehicle Navigation

Scale AIImageSegmentation

Identifying Drivable Space: Precisely segmenting the road from sidewalks, curbs, and grass. Understanding Surroundings: Detecting and locating pedestrians, cyclists, vehicles, and other obstacles at the pixel level. Lane Detection: Precisely marking lane boundaries.

Identifying Drivable Space: Precisely segmenting the road from sidewalks, curbs, and grass. Understanding Surroundings: Detecting and locating pedestrians, cyclists, vehicles, and other obstacles at the pixel level. Lane Detection: Precisely marking lane boundaries.

2020 - 2023

2D Bounding Box Annotation for Urban Pedestrian Detection Dataset

Scale AIImageBounding Box

To create a high-quality, diverse dataset to train and validate a perception model for an autonomous vehicle's pedestrian detection system, specifically for dense urban environments.

To create a high-quality, diverse dataset to train and validate a perception model for an autonomous vehicle's pedestrian detection system, specifically for dense urban environments.

2017 - 2020

Education

Work History