Language and Image Analysis
Two smaller capabilities sit alongside document reading: identifying the language of a piece of text, and identifying what is in an image.
Where to find it
Architect Panel → Data:
- Datastores — where results are stored
- Documents — uploaded images
Architect Panel → Communication:
- Message Log — correspondence that may need routing by language
Language detection
Given text, it returns the most likely language with a confidence score, and the alternatives it considered. Useful for routing correspondence to somebody who speaks it, deciding which language to reply in, or tagging free-text records.
Use the confidence score
Short text is genuinely ambiguous — a three-word message may plausibly be several languages, and the model is right to be unsure. Acting on a low-confidence result produces exactly the error a customer notices, such as a reply in the wrong language.
Set a threshold, and below it fall back to a default rather than guessing. A confident wrong answer is worse than an admitted uncertainty.
Send a sample, not everything
Only a limited amount of text is analysed at once and longer text is trimmed automatically. A representative sample — the opening of a message rather than an entire thread with quoted replies in another language — gives a better answer than sending everything.
Image analysis
Given an image, it detects what is in it. Three uses come up repeatedly:
- Tagging photographs so they can be found later.
- Reading text within an image — a sign, a serial plate, a label.
- Screening user-uploaded images before they are published.
Screening is assistance, not a guarantee
If you are using it to keep unsuitable content off a public page, understand what you are getting. It will catch a good deal and it will not catch everything, and it will occasionally flag something harmless.
Use it to prioritise a review queue rather than to publish automatically. "The system checked it" is not a defence you would want to rely on.
Tagging is where it is safest
Because a wrong tag is a minor annoyance. A photograph tagged with what it appears to contain makes an archive searchable in a way filenames never do, and nobody is harmed by an occasional odd tag.
Store the confidence, not just the answer
Whatever you use these for, keep the score alongside the result. It lets you tune your threshold later using real data, and it lets somebody looking at an odd result see whether the system was confident or guessing.
Discarding it means every result looks equally authoritative, which is the opposite of what these produce.
Tell people when it is in use
Where automated analysis affects how somebody is treated — routing, screening, prioritisation — that belongs in your privacy information. It is also simply fairer, and it is a question that gets asked.
Worked example
A service detects the language of inbound messages and routes anything above 80% confidence to a speaker of that language; below that it goes to the general queue. A dashboard shows the distribution of scores, and after a month the threshold is lowered to 70% because almost nothing fell between the two.
Recommendations
- Set a confidence threshold and a fallback below it.
- Store the score with the result.
- Use screening to prioritise review, never to publish.
- Disclose automated analysis where it affects people.