Midv699 Verified -

| Category | Minimum Requirement | Supporting Evidence | |----------|--------------------|---------------------| | Identity | Government‑issued ID, or verified email/social profile that matches the account name. | Scanned ID (blurred except for name & photo) or OAuth token from a trusted provider (Google, GitHub, etc.). | | Activity | ≥ 200 cumulative contribution points (posts, commits, tutorials, or moderation actions) over the past 12 months. | Exported contribution log or link to the user’s activity page. | | Quality | Average rating of ≥ 4.5/5 on contributions, as judged by peer reviews or upvotes. | Screenshots of rating dashboards or a summary report. | | Community Conduct | Zero “serious violations” (spam, harassment, plagiarism) in the last 24 months. | Moderator clearance or a clean conduct report. | | Technical Proficiency (optional but highly recommended) | Demonstrated mastery of at least one core MidV699 technology stack (e.g., MidV699 SDK, API, or plugin framework). | Public repository, tutorial series, or certification badge. |

Note: The criteria are periodically reviewed; a user may be asked to provide updated documentation during re‑verification (annual cadence). midv699 verified

The dataset supports training for text recognition. By using the verified field-level annotations, models can be fine-tuned to extract key-value pairs. This is particularly useful for automating form-filling processes where specific data points (e.g., ID Number) must be isolated from the rest of the text. | Category | Minimum Requirement | Supporting Evidence

The term "verified" in the context of MIDV699 implies a rigorous annotation process. Each frame is labeled with: The dataset supports training for text recognition

The verification process ensures that ground truth labels align with the visual data, mitigating the noise often found in scraped web datasets. This high-fidelity ground truth is essential for training models to handle edge cases, such as holographic reflections or blurred text.