Edwin Prabhahar

Key Skills

Software

Appen

Clickworker

CrowdSource

Lionbridge

Remotasks

Scale AI

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Text

Video

Top Label Types

Action Recognition

Audio Recording

Bounding Box

Classification

Data Collection

Evaluation Rating

Land Cover Classification

Mapping

Object Detection

Text Summarization

Transcription

Freelancer Overview

With over 12 years of experience in e-publishing and content management, I have developed strong expertise in data preparation, annotation, and quality control for scientific and technical content. My background includes hands-on work with XML processing, LaTeX manuscript handling, PDF generation, image editing using GIMP, and meticulous author corrections—skills that translate directly to high-quality data labeling and annotation for AI training datasets. I have led teams to ensure accurate data processing, trained staff on workflow best practices, and maintained strict adherence to production schedules and quality standards. My experience with complex document structures, equation processing, and image editing has given me a keen eye for detail, making me well-suited for roles involving data curation and annotation in domains such as scientific publishing, document digitization, and computer vision. I am proficient in Microsoft Office and various publishing tools, and I am committed to delivering precise, reliable training data for AI and machine learning applications.

Labeling Experience

Multilingual wake up word recording Collection

AppenAudioClassification

The "Multilingual Wake Up Word Recording Collection" project on Appen (also referred to in similar forms as wake word / hotword recording or voice command collection projects) is a data collection initiative aimed at gathering high-quality audio samples to train and improve automatic speech recognition (ASR) systems, voice assistants, keyword spotting, and wake word detection models for multilingual voice AI applications.

The "Multilingual Wake Up Word Recording Collection" project on Appen (also referred to in similar forms as wake word / hotword recording or voice command collection projects) is a data collection initiative aimed at gathering high-quality audio samples to train and improve automatic speech recognition (ASR) systems, voice assistants, keyword spotting, and wake word detection models for multilingual voice AI applications.

2022 - 2022

Education

Work History