#moderation

Strip, extract and count emoji in any text. The strip endpoint removes every emoji from a string — or replaces each one with a marker you choose — and gets multi-code-point emoji right: ZWJ sequences like the family 👩‍👩‍👧‍👦, skin-tone modifiers (👍🏽), country flags (🇩🇪), keycaps (1️⃣) and variation selectors all count as a single emoji, so nothing is left half-deleted. The extract endpoint lists every emoji it finds with its position in the text and returns per-emoji and unique counts, ideal for analytics and moderation. A bare ©, ® or ™ is deliberately left alone unless it carries an emoji variation selector, and plain digits are never touched. Perfect for cleaning user input before search indexing or storage, sanitising usernames and display names, moderation and analytics, and preparing text for systems that choke on emoji. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This cleans and extracts emoji from text; to look an emoji up by name or shortcode use an emoji database API, and to count graphemes use a text-segmentation API.

api.oanor.com/emojistrip-api

Detect and censor profanity in user-generated text across 24 languages — for comment moderation, chat filtering, username and form validation, and trust-and-safety pipelines. Send any text and get back whether it contains profanity, the exact bad words found and which languages they belong to; or get the text back with every bad word masked (choose your own mask character). Matching is word-boundary aware (so "Scunthorpe" and "Penistone" are not flagged) and normalises common leetspeak (sh1t, @ss) before matching. Target a specific language (or several) or scan all 24 at once. Powered by the well-known LDNOOBW word lists, bundled in — so the service is fully self-contained: no third-party calls, no rate limits, always available. Live, no cache. 4 endpoints. No upstream key.

api.oanor.com/profanity-api

Moderate images automatically with on-device machine learning. Classify any image across five categories — neutral, drawing, sexy, porn and hentai — and receive per-class probabilities, the top class, a combined NSFW score and a clear verdict (safe, questionable or nsfw). A simpler check endpoint returns a single safe/unsafe decision against a threshold you choose, ideal for upload gates and user-generated-content pipelines. Supply an image by public URL, base64 or a raw binary request body; only public http/https URLs are accepted and private or internal hosts are blocked, and large images are downscaled automatically. Runs locally on TensorFlow (NSFWJS / MobileNetV2) — no third-party upstream and no per-image cloud cost — with a warm model that keeps inference fast. Ideal for community platforms, marketplaces, dating and chat apps, and any service that accepts user images.

api.oanor.com/nsfw-api