Data Annotation and Labeling Service in Japanese

Low-Quality Data Annotation and Labeling in Japanese

You wish to outsource data annotation and labeling in Japanese for your AI development, but you have concerns about the quality of the labeled data delivered.

Security Concerns About Outsourcing Overseas

You are worried that your sensitive data could be compromised when outsourcing data annotation and labeling in Japanese to oversea companies.

Lack of Companies that Can Handle Data Annotation and Labeling Flexibly

Your ongoing project requires frequent changes to data annotation and labeling requirements, but you can’t find an outsourcing company that can adapt accordingly.

Lack of Labeled Data

You’re planning to develop a new AI system, but a lack of labeled data is causing delays.

We Completed Large-Scale, High-Difficulty Segmentation of Tens of Thousands of Images in a Few Months.

In this project, we conducted large-scale image segmentation on a tight schedule to enhance the accuracy of image recognition AI. By revolutionizing technical processes and establishing a robust operational structure, we processed tens of thousands of complex images with extreme precision. This refined approach allowed us to balance quality, scale, and speed at a high level for the client’s critical needs.

We Developed Specifications Based on Expert Knowledge and Provided Comprehensive Upstream Support.

For this project focused on improving crop detection accuracy, we provided end-to-end support from initial specification development to data annotation. Working closely with the client, we rigorously defined growth stages based on the state of plant organs like petals and fruits. By engaging early to codify expert knowledge into actionable guidelines, we achieved a highly significant improvement in AI detection accuracy.

We Investigated, Selected, and Customized Data Annotation Tools, and Built Optimal Project Environments.

We supported AI development by researching, setting up, and customizing data annotation tools. To meet the specific request for data annotation from multiple perspectives based on complex specifications, our engineers implemented a custom tool with optimized settings. This comprehensive technical intervention led to significant reductions in labor hours and the delivery of exceptionally high-density datasets.

Strengthening Quality Assurance through Joint Research with a Designated National University

At Nextremer, we are conducting joint research with the University of Tsukuba to improve the reliability of data annotation and labeling work, which directly impacts the quality of the labeled data used to develop AI. Our research focuses on standardizing quality assurance methods for data annotation and labeling tasks, developing guidelines, and verifying reproducibility by integrating the academic insights framework with real-world operations. We maximize the success rate of our clients’ AI development by continuously updating this reliable data creation system.

Image Data

Object Detection
Semantic Segmentation
Image Classification
Pose Estimation
Facial Expression Recognition

Video Data

Action Recognition
Object Tracking
Video Classification

Text Data

Morphological Analysis
Named Entity Recognition (NER)
Text Classification

Audio Data

Speech Transcription
Anomaly Detection
Audio Classification

We will change the "data preparation" that accounts for 80% of AI projects. Japanese Data Annotation &
Dataset Construction Solution

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Low-Quality Data Annotation and Labeling in Japanese

Security Concerns About Outsourcing Overseas