Data Annotation Specialist (NLP & Computer Vision) 

I create high-quality labeled datasets for training AI models across text and image-based tasks using industry-standard tools like Label Studio. 

📊 5 Completed Annotation Projects

🧠 NLP & Computer Vision Expertise

⚙️ Tools: Label Studio, JSON, COCO 

🧠 MY ANNOTATION WORKFLOW 

• Follow clear and consistent labeling guidelines

• Ensure accurate and context-aware annotations

• Handle edge cases and ambiguous data carefully

• Maintain structured dataset formatting (JSON / COCO)

• Perform quality checks before final export

Project Title: Urban Street Object Detection Dataset (Bounding Box Annotation)

Annotated 141 objects across 10 images using COCO format. 

Project Description:

This project involves annotating urban street images for object detection using bounding boxes. The dataset includes real-world street scenarios featuring pedestrians, vehicles, and animals.

A total of 10 high-resolution images were annotated using Label Studio, following strict annotation guidelines to ensure accuracy, consistency, and completeness.

The goal of this project is to simulate real-world data annotation tasks used in training computer vision models for autonomous driving and smart city applications.

Annotation Details:

Annotation Type: Bounding Box

Tool Used: Label Studio

Export Format: COCO JSON

Classes:

• Person

• Car

• Bicycle

• Dog

• Traffic Light

Total Images: 10
Total Annotations: 141

Sample annotated images demonstrate accurate object detection, proper bounding box placement, and adherence to annotation guidelines. Each object instance is clearly labeled, including partially visible and overlapping objects. 

2. Project Title: Image Classification – Multi-Class Object Recognition 

Classified images into multiple categories including animals, objects, and humans to support visual recognition systems. 

Annotation Details:

Annotation Type: Image Classification 

Tool Used: Label Studio

Export Format: JSON

Classes:

• Person

• Car

• Food

• Dog

• Cat

Total Images: 13 images across 6 classes using structured classification workflow.

1. Project Title: Sentiment Analysis 

Classified text data into sentiment categories to help AI systems understand user opinions and emotions. 

Annotation Details:

Annotation Type: Text Classification (Sentiment Analysis)

Tool Used: Label Studio

Export Format: JSON

Classes:

• Positive

• Negative

• Neutral 

Metrics: Annotated 10 text samples across 3 sentiment classes 

2. Project Title: Spam Detection 

Labeled messages as spam or not spam to support filtering and content moderation systems.  

Annotation Details:

Annotation Type: Text Classification (Spam Detection) 

Tool Used: Label Studio

Export Format: JSON

Classes:

• Positive

• Negative

• Neutral 

Metrics: Annotated 10 messages using consistent spam detection criteria 

3. Project Title: Named Entity Recognition (NER) 

Annotated text by identifying entities such as names, organizations, locations, and dates for structured data extraction.   

Annotation Details:

Annotation Type: Text Classification (Named Entity Recognition-NER) 

Tool Used: Label Studio

Export Format: JSON

Entities:

• Person

• Organization

• Location

• Date 

Metrics: Annotated 10 text samples with multiple entity spans.