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Achievements and Case Studies (Annotation Site)

Case Studies

We support projects for many companies, including GAFAM.

We are participating in AI development projects in a wide range of fields such as the medical industry, automotive industry, IT industry, etc., with a thorough security system and high-precision annotation.



Translation, Documentation, and Annotation Achievements

Achievements and Case StudiesCase Study

CASE 01
Advanced AI Development Project for Medical Devices
For Medical Device Manufacturing Companies

Required tasks
  • AI development of advanced medical devices aimed at supporting surgery and diagnosis.
  • Perform instance segmentation of several labels using actual data such as endoscopic images and X-ray images.
Customer's
Challenges
  • I want to avoid remote work and outsourcing overseas in order to handle highly confidential medical data.
  • There is concern about the compatibility between the source data and the tools used by the subcontractors.
Our
Solution
  • We have built a dedicated security room for this project within our office. It will be operated on-site. By allowing only project members to enter the room, we have ensured the security of the data and the confidentiality of the project itself.
  • By properly managing the version control of the tools used, we have unified the environment for our customers and annotators. This ensures data integrity.
Number of tasks
 10,000 items
Work Period
 2 months
This is the point
  • Operate on-site using the security room within Human Science Co., Ltd. that has obtained ISMS.
  • Achieve thorough data management with comprehensive security management and worker education.
  • Flexible support for introducing new annotation tools and updating versions.

CASE 02
Autonomous Driving AI Accuracy Improvement Project
For AI Technology Development Manufacturers

Required tasks
  • Annotation for improving autonomous driving technology. Tagging is performed by specifying objects and areas based on drive recorder footage.
Customer's
Challenges
  • Planning for long-term operation, but the work content is monotonous and the retention rate of annotators is low. Additionally, even if excellent annotators are nurtured, they leave quickly.
Our
Solution
  • We have selected and organized a team of qualified personnel for this project from our contracted annotators.
  • Regular meetings are held within the team. We stabilize quality and productivity by creating a system that does not leave annotators' questions unanswered.
  • Regularly implemented reassignment of responsibilities and team movements. By making changes to the environment while ensuring quality, we were able to maintain the motivation and sense of accomplishment of the annotators.
Number of tasks
 Over 6,000 items
Work Period
 6 months or more
This is the point
  • Human Science will organize a team with personnel suitable for the project's tasks from resources directly contracted by Human Science.
  • Create changes and a sense of achievement even after the start of the project to maintain the annotators' motivation. Stabilize the quality of work by connecting to long-term employment.

CASE 03
AI Assistant User Request Understanding Improvement Project
Global IT Company

Required tasks
  • To ensure that the AI assistant correctly understands the user's voice request and can perform the desired action.
  • Workers evaluate the understanding of AI by tagging each action taken by AI.
Customer's
Challenges
  • I want to build a team of 40 members within 2 months.
  • Working in a security room is essential due to the highly confidential nature of the project.
  • The task requires a high level of difficulty, as well as insight and judgment, so we want to proceed with the work only with skilled annotators. Training for annotators is essential before starting the actual work.
  • It is difficult in terms of cost to secure resources from our own company.
Our
Solution
  • First, we started the project in our existing security room and then newly established a new security room where 40 people can work in 1.5 months.
  • We have established a team structure and training program based on the proficiency of the annotators. By actively sharing and updating knowledge, we have been able to improve and stabilize the quality.
Number of tasks
 About 450,000 items
Work Period
 6 months
This is the point
  • Human Science provides security rooms that meet your standards. We also respond promptly to expansions.
  • Thorough security education for annotators. Achieve project management that meets high security standards in both resources and environment.
  • By sharing information and communicating closely among members, we support the proficiency of annotators. This enables cost reduction through shortened training periods and improved productivity.

CASE 04
Project to improve OCR text recognition accuracy
Global IT company

Required tasks
  • To improve the recognition accuracy of OCR, convert text areas from images such as maps and restaurant menus into data that AI can understand.
  • The operator manually selects the text area and adds the correct information to each one.
Customer's
Challenges
  • I want to ensure maximum uptime within the deadline, but our own resources alone are not enough.
  • Due to the difficulty of the task, there have been many retirements from the resources that were once hired for training. The progress of the project is challenging.　　
Our
Solution
  • Designed and implemented a new specialized recruitment test for projects. By forming teams only with successful candidates, we reduced retirements and improved operational efficiency.
  • We analyzed the human tendencies of the annotators who performed well in training and actively hired resources with similar tendencies.
  • We have organized a team with resources that can understand English guidelines and materials as they are. By eliminating the process of translating documents, we have achieved cost reduction.
Number of items
 22,000 cases
Work Period
 1,600 hours/month
This is the point
  • The skills in document creation and the resources for multilingual support that Human Science Co., Ltd. has cultivated so far have also been utilized in the test creation and team organization for this project.
  • We have achieved a high operating efficiency that exceeds the initial expected standards as a result.

CASE 05
Contract Confirmation AI Automation Project
Global IT Company

Required tasks
  • Automating the process of reviewing contract contents by analyzing text.
  • The worker reads the contract documents, extracts and categorizes specific phrases and expressions, and performs labeling. Understanding of technical terms and defining complex labeling is required.
Customer's
Challenges
  • Internal resources are insufficient, and the establishment of a system to mass-produce teacher data is not progressing.
  • I don't know where to start to conduct a PoC (Proof of Concept).
  • I want to consult with experienced individuals for the establishment of work rules, standardization of knowledge, and creation of management mechanisms.
Our
Solution
  • We have dispatched one experienced annotator from our company's resources to work at the client's site.
  • We listened to your challenges and together we have documented the work process and criteria for decision-making.
  • We have developed a mechanism to concretize the management challenges for the future expansion of annotations and ensure their continuity.
Number of items
 About 200 items
Work Period
 3 months
This is the point
  • By dispatching an experienced project manager from Human Science Co., Ltd., we can visualize current and future challenges.
  • By being stationed in the customer's environment, we achieve both detailed support and data confidentiality. We have contributed to the establishment of a system for expanding the annotation structure.

CASE 06
AI PoC Project for Automatic Determination of Internal Tissue Areas
For Medical Device Manufacturers

Required tasks
  • Instance Segmentation of CT Slice Images
Customer's
Challenges
  • Instance segmentation of CT slice images.
  • We attempted to internalize data annotation using available engineers, but were unable to keep up with the maintenance of annotation specifications, resulting in quality variations and production issues, causing delays in progress as planned.
Our
Solution
  • Rapid project launch utilizing our contracted data annotators (experienced in medical data annotation).
  • Created an annotation specification document from sample data provided by the client.
Number of items
 About 2,000 items
Work Period
 2 weeks
This is the point
  • Create an annotation specification document through customer-provided annotated data and Q&A.
  • In addition to the above specifications, we have utilized annotated sample data as a training reference for difficult-to-explain sections, achieving a shortened training period for data annotators.
  • Our carefully selected contract data annotators and shortened learning period have ensured high productivity from the early stages of project launch. Despite the difficulty of the task, we have been able to meet our clients' deadlines and have received high praise from them for both quality and delivery time.

CASE 07
Conversation Emotion Detection AI Project
For Content Creation IT Companies

Required tasks
  • Label 8 emotions for conversational text.
Customer's
Challenges
  • As the annotation work has been done by a single engineer within the company, the creation of training data has not progressed. Therefore, we are considering outsourcing, but due to the ambiguous and difficult nature of annotation work, there may be individual differences in labeling and we are concerned about whether we can consistently create high-quality training data.
  • There is no experience or know-how in creating standard documents to suppress the variation of labeling and stabilize the quality when working with multiple people or outsourcing.
Our
Solution
  • Before entering into an outsourcing contract with our clients, we conduct a trial and have our clients evaluate the quality.
  • We create data annotation guidelines at our company.
  • Adopting triple pass (three people annotate the same data, select and determine the label by majority vote.)
Number of items
 20,000 cases
Work Period
 About 2 months
This is the point
  • During the trial, we were able to create an annotation specification that met the client's requirements, despite the high level of ambiguity, while receiving Q&A, communication, and feedback from the client. Additionally, the specification was useful for regular additional learning within the client's organization.
  • By making frequent partial payments, we can timely respond to feedback and requests from our customers, alleviating any concerns they may have about the quality of our services.
  • In addition to triple pass, by conducting PM checks and providing timely feedback to workers, and holding regular meetings, we have received high praise from our clients for ensuring stability and consistency in quality while suppressing variations and biases in worker judgments that are common in ambiguous language annotations.

CASE 08
Mechanical Operation Analysis AI Project
For Machine Tool Manufacturers

Required tasks
  • Key Point Data Annotation for Machine Operators
Customer's
Challenges
  • Without the know-how to produce data annotations in-house, we cannot establish a system to ensure stable quality and productivity.
  • Due to the high ambiguity of the annotation position, there were significant individual differences in the point annotation position, and even when aligning people within the company to perform annotation, there was a large variation in quality and a high number of rework occurrences.
  • Having trouble grasping the key points and guidelines for creating a manual to reduce variations in data annotation.
  • High confidentiality of data, please use customer preparation tools and perform work domestically.
Our
Solution
  • Launched project team quickly with our registered domestic data annotators who have received security education.
  • As we proceed with our work, we accumulate responses and criteria for dealing with edge cases, and provide feedback to the manual provided by our customers.
Number of items
 3,000 files
Work Period
 3 weeks
This is the point
  • At the start of the project, the Project Manager (PM) actually performs the work and works with the client to clarify any questions and answers (Q&A) about the work specifications. This allows us to understand the finer details that cannot be captured in the manual.
  • By accumulating and documenting knowledge and information such as detailed points about work, judgment criteria for edge cases, and utilizing them for worker education, we were able to shorten education time and successfully launch a smooth team and stabilize quality.
    By sharing the accumulated information with our clients, we were able to help them create work manuals and acquire knowledge for outsourcing data annotation.

Other Case Studies

  • Natural Language Processing
    Data Generation for AI Assistants
    Project to improve the accuracy of AI assistants. We assigned native speakers to generate a large amount of natural text that is likely to be spoken by general users as requests to the AI assistant.
  • Map Information
    Improved map app route proposal feature
    Map app user satisfaction improvement project. We have mass-produced high-quality training data with more accurate information by evaluating whether the app's recognized location information and suggested routes are appropriate.
  • OCR Text
    Improved Image Text Recognition Accuracy
    Text area extraction from images. Request from overseas companies. We organized an annotator team within 3 business days, consisting of resources that can understand and operate English operation manuals and feedback as they are. We completed the project within the deadline without spending time on translation or interpretation.
  • Speech Recognition
    Creating Teacher Data for Voice Reading
    Project for creating teacher data using multilingual speech synthesis. Project team composed of native speakers of each language. Creating voice data in Japanese, English, Chinese, and Korean. This is a case where the resources cultivated in our translation business were helpful.
  • Machine Translation Evaluation
    Creating Machine Translation Training Data
    A project to evaluate the output of machine translation and improve the quality of training data. It will contribute to improving translation accuracy by collaborating with natural language processing. This is a case where both our translation business experience and knowledge of natural language processing with AI/annotation have been utilized.
  • Intent Extraction
    Search Engine Accuracy Evaluation
    Project to improve search engine understanding. Workers evaluated whether the browser was displaying appropriate results for user search inputs, one by one.

Useful MaterialsDownload

  • Introduction to Annotation Services

    Download

  • Three points to keep in mind when outsourcing

    Download


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