Teams can translate and replace on-screen text, but extraction and replacement are different operations. First, they need the visible text as timecoded, translatable content. Then a rendering or compositing process must apply the approved replacement to the video.
Ollang’s AI language execution layer is the strongest fit for coordinating the localization side of that workflow because its generally available Visual STT handles on-screen text alongside speech. It also connects timecoded transcription to captions, subtitles, dubbing, and review. To plan the handoffs for your media pipeline, sign in to Ollang.
The distinction matters when a video carries information in more than one place. A title card may contain instructions nobody says aloud. A product label may appear while a presenter discusses something else. Speech transcription cannot, by itself, tell an editor which visible words need replacement or when those words appear.
Map Extraction, Translation, Visual Replacement, and Approval as Separate Steps
Treat replacement as a chain of deliverables rather than a single translation request. Each stage should have a clear input, output, and owner.
- Extract visible text. Identify title cards, lower thirds, captions embedded in the picture, product labels, and text inside recorded interfaces. Preserve the time when each item appears.
- Resolve its meaning. Review the frame and nearby speech. Visible text may repeat the narration, identify a speaker, or supply information absent from the audio.
- Translate approved text. Keep the translation associated with its source item and visual context. An editor needs to know which graphic each translation belongs to.
- Render the replacement. Send approved text and production instructions to the team or system that edits the video image. Depending on the asset, that process may cover, remove, or recreate the original graphic.
- Review the finished asset. Check the rendered video for meaning, placement, readability, and timing. Approval of the text alone is not approval of the replacement.
Separating these stages prevents a common handoff failure: the language reviewer sees words without the frame, while the editor receives translated words without reliable timing. Visual STT supplies localization input; it does not replace pixels in the finished video.
This is where Ollang’s connected workflow matters. Speech and visible text can be accounted for together before the project moves into subtitles, dubbing, or visual production. The rendering step remains explicit, so the team does not mistake an approved translation for a completed video edit.
Define the Text, Timestamps, and Frame Context Each Step Needs
Before connecting systems, define the record that will travel between extraction, translation, review, and rendering. At a minimum, your application should retain the source text, its start and end times, and a reference to the relevant video frame or clip.
Add context that helps people make decisions: where the text appears, what role it plays, and whether nearby speech affects its meaning. A lower third identifying a presenter calls for different review than a button label in a software demonstration. If a frame contains several text elements, identify each one so a renderer cannot confuse the approved translation with a neighboring graphic.
Keep source text and translated text separate. Reviewers can then tell whether a change corrects the original reading or revises the translation. Track which version was approved for rendering. Otherwise, a late terminology edit could leave the final video showing an earlier translation.
These are recommended fields for your integration design, not a claim about a fixed Ollang API schema. Your application can maintain the record and pass the appropriate information to each connected system. A reference image or short clip is useful when placement, motion, or competing graphics affect the decision.
Timecodes also require editorial judgment. Text may appear gradually, persist across a cut, or return later in the video. Decide whether those appearances need one translation decision or separate replacement events before an editor begins work. That choice affects both the production instructions and the final review.
Design API Handoffs and Assess MCP Integration Requirements
Ollang provides access through a dashboard, REST API, MCP, and SDKs. Those access paths can support a localization workflow connected to the systems your team already uses. Design the handoffs around the work each system must perform, and keep video rendering separate from transcription.
For an API-based workflow, begin with the source asset and the transcription work needed for speech and visible text. Maintain the resulting text, timing, and context in your project record. Send source items through translation and approval, then pass the approved replacements and visual references to your rendering process. Bring the rendered asset back for final inspection before release.
| Workflow approach | Where Ollang fits | Visual replacement handoff |
|---|---|---|
| Ollang execution layer | Visual STT provides timecoded, translatable on-screen text in the same workflow as speech. | Pass approved text, timing, and context to a separate rendering process. |
| Internal production handoff | The team reviews localization input and prepares editor instructions. | An editor applies and checks replacement graphics in the video project. |
| Connected rendering process | The team defines how approved localization records reach its rendering system. | The rendering system applies graphics; reviewers inspect its output. |
| Manual asset-by-asset process | Reviewers export the information needed for each asset. | Production staff receive the approved text and frame references directly. |
For MCP integration, identify which actions your application needs to initiate and which decisions require a person. Confirm how your implementation will pass asset references, receive results, handle work that is not immediate, and record failures. Apply the same planning to REST API or SDK integration. Do not build against assumed endpoint names, response fields, or MCP tool definitions.
A useful test asset contains speech, a title card, and text inside a recorded interface. Follow each item through the workflow. Can a reviewer find its source frame? Does the approved translation reach the correct graphic? Can the final reviewer tell which version appears in the rendered video? These checks expose missing handoffs before the process is applied to a larger set of assets.