Junye C.

Key Skills

Software

No software listed

Top Subject Matter

Computer vision image annotation

Computer vision image segmentation

Document AI and information extraction

Top Data Types

Image

Document

Top Task Types

Bounding Box

Segmentation

Freelancer Overview

Data Annotation Specialist (Bilingual) — Image Bounding Box Labeling (in progress). Education includes Bachelor’s Degree, N/A. AI-training focus includes data types such as Image and Document and labeling workflows including Bounding Box, Segmentation, and Entity (NER).

Labeling Experience

Data Annotation Specialist (Bilingual) — Document Entity Recognition Labeling (in progress)

Document

Junye Chen performs document entity recognition labeling to extract key information from document images. They label important entities to enable downstream tasks such as information extraction and structured data creation. They apply bilingual context and careful review to improve annotation reliability. • Identify and label document entities for extraction • Support document information structuring from visual inputs • Use detailed QA checks to improve correctness • Leverage bilingual understanding for cross-lingual verification

2026 - Present

Data Annotation Specialist (Bilingual) — Image Segmentation Labeling (in progress)

ImageSegmentation

They also conduct image segmentation labeling to support pixel-level understanding required by advanced AI models. This includes generating accurate masks that align with object boundaries and dataset guidelines. Quality control is central to maintaining uniform labeling across many samples. • Create segmentation annotations aligned to object contours • Follow dataset standards for mask quality • Verify labels for boundary correctness • Ensure consistent labeling across large volumes

2026 - Present

Data Annotation Specialist (Bilingual) — Image Bounding Box Labeling (in progress)

ImageBounding Box

Junye Chen performs precise image bounding box labeling to create accurate training annotations for computer vision tasks. They ensure consistency across large-scale datasets and verify label quality against expected visual details. Their work emphasizes high accuracy to support reliable AI model training. • Label object locations using bounding boxes • Maintain annotation consistency and accuracy at scale • Perform cross-checking to reduce labeling errors • Apply meticulous attention to detail for quality assurance

2026 - Present

Education

Work History