Characters Contour Segmentation Dataset

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Project Overview:

Objective

As a leading data collection and annotation company, we successfully executed a project to create a comprehensive dataset for character contour segmentation. This dataset is instrumental in enhancing character recognition, and typography analysis, and aiding in the development of digital font creation tools.

Scope

Our project entailed the establishment of a diverse repository of character images from multiple languages and scripts. Each character was meticulously annotated, focusing on the precise contour, which is critical for advanced character recognition applications.

Sources

Typography Institutions: We collaborated with typographical institutions and design schools, gaining access to a wide range of font styles and character designs.
Open-source Font Libraries: Our team utilized existing font resources to extract character images, ensuring representation from a variety of scripts.
User Submissions: We also launched a platform for users to contribute character images, significantly aiding in including rare and indigenous scripts.

Data Collection Metrics

Total Characters Collected and Annotated: 345,000
Typography Institutions’ Contributions: 105,000
Open-source Libraries: 160,000
User Submissions: 80,000

Annotation Process

Stages

Contour Segmentation: Our team meticulously segmented the contours of each character, enabling precise boundary identification.
Script and Language Identification: Each character was labeled with its respective script and language.
Font Style Tagging: We included metadata about the font style for each character.

Annotation Metrics

Characters with Contour Segmentations: 345,000
Script and Language Tags: 345,000
Font Style Metadata: 345,000

Quality Assurance

Stages

Segmentation Verification: Automated algorithms were employed to verify the accuracy of contour segmentation.
Metadata Validation: We engaged typographical experts for accurate script, language, and font-style tagging.
User Privacy: We prioritized user privacy by ensuring that submitted character images were free from identifiable information and adhered to privacy standards.

QA Metrics

Segmentation Review Cases: 34,500 (10% of total)
Metadata Authenticity Checks: 69,000 (20% random sampling)
User Data Privacy Audits: 80,000 (for user submissions)

Conclusion

Our Characters Contour Segmentation Dataset Initiative is a testament to our expertise in data collection and annotation across diverse fields. This project not only furthers our understanding of character design across different scripts but also solidifies our position as a vital contributor to the future of typography, character recognition, and digital design advancements.

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