Offline Handwriting Translation Test Plan

Direct answer

A handwriting test must begin with the possibility that the selected OCR path does not recognize handwriting. Do not infer handwriting support from a printed-text demo, a language selector, or a successful translation of text that a person typed manually. `OCR-DEDICATED-01` describes structured text recognition output such as blocks, lines, and elements; it does not establish that a given library or model supports handwritten signs. `BENCHMARK-01` requires raw representative records before publishing quality results. The test goal is to discover what the exact build does and preserve uncertainty, not to promise handwriting translation. Prepare an authorized, versioned image set with a fluent reader's source transcription, image-region labels, and target-language reference reviewed independently. Include printed, stylized, and handwritten examples as separate groups. Within the handwritten set, vary writing size, stroke overlap, slant, orientation, background, and partial occlusion as descriptive fixture labels. Do not claim the corpus represents a language or writing population unless its sampling is documented. Avoid recording real people's handwriting without consent, since names, addresses, and private notes may be identifiable. Record camera or import entry point, image orientation and crop, preprocessing revision, OCR component and configuration, model/runtime identity if applicable, source/target direction, network state, and output. First compare recognized source text with the reference. Only after transcription is checked should the translation be compared to the target reference. If no literal text is returned, mark recognition unsupported or failed in that tested configuration; do not let a language model reconstruct missing characters from context and present them as observed handwriting. Keep uncertainty visible. A user should be able to view the source image, correct the transcription, reject an unreadable span, and retry a closer crop. Record every correction as user-provided rather than model output. A fluent translation can still be based on incorrect recognition. For dates, amounts, names, addresses, negation, and safety-critical wording, require source review and do not auto-normalize ambiguous characters. A test can record whether the pipeline responds to a chosen fixture; it cannot declare the whole handwriting modality reliable from a handful of examples. Use a predeclared matrix: clear print as a control, cursive or brush writing if within scope, mixed printed/handwritten content, low contrast, rotated pages, clipping, and blank regions. Include failure states such as camera permission denial, failed image decode, OCR returns no text, partial line, parser rejection, translation unavailable, cancellation, export, and cleanup. Check that an ImageProxy or temporary image resource is closed on failure when the selected capture path uses one. Do not include source images in analytics or default logs. Report counts and methods only if raw fixture records and scoring rules are retained; otherwise present a qualitative case ledger. State which writing styles and conditions were tested, which were not, and whether OCR output was manually corrected before translation. The resulting page helps a developer decide whether to show “unreadable” and request another image, not whether an unspecified translator supports handwriting. Avoid model rankings, language promises, and accuracy percentages until evidence exists.

A traveler photographs a handwritten direction note

The note contains a street name and a short instruction written by hand. The traveler needs a readable translation, but no reviewed source text is available yet and handwriting support is unverified.

Decision

Preserve the image, separate literal transcription from translation, mark unreadable characters, and allow a human to correct or reject the source before translating.

Expected outcome

The test states whether the exact build recognized the fixture and what remained uncertain without claiming broad handwriting capability.

Evidence to collect

The claim becomes reviewable only when the following evidence is attached to the same artifact and test run:

• Handwriting image and text fixtures
• Literal recognition record
• Translation and correction outcome

Task quality needs a versioned corpus and acceptance rules. Fluent sample output is not an accuracy result.

Implementation workflow

1. Assemble a reviewed handwriting fixture. Use an authorized image, fluent-reviewed source transcription, labeled regions, and a separately reviewed target reference. Keep print and handwriting conditions in separate groups.
2. Record exact capture and runtime inputs. Log the camera or import route, orientation, crop, preprocessing, OCR component and configuration, model/runtime if any, language direction, and connectivity so another reviewer can reproduce the same conditions.
3. Evaluate literal recognition first. Compare recognized characters and spans with the source transcription. Preserve missing and ambiguous characters; do not reconstruct them silently from semantic context.
4. Review translation only after source correction. Let a user inspect, edit, reject, or retry a clearer crop. Mark corrections as user supplied and compare the resulting translation with an independent target reference.
5. Test failures, privacy, and cleanup. Exercise permission denial, decode failure, no/partial OCR, parser rejection, cancellation, export, and resource closure. Keep private writing out of default logs.

Keep each transition observable. A failure should identify the stage, artifact, runtime, and recovery action without logging private user content.

Acceptance criteria

• Every test records exact image conditions and distinguishes printed-control results from handwritten examples.
• Unreadable or missing source characters remain uncertain until a human correction; translation does not masquerade as observed text.
• Claims are limited to the recorded build and fixtures, with failures and untested writing conditions stated.

Decision table

Failure patterns to prevent

• Calling printed-text results handwriting support
• Hiding unreadable input

Also prevent silent fallback, unpinned artifacts, missing cancellation, and conclusions that combine unlike configurations. Store unsuccessful runs alongside successful ones.

Minimum reproducibility record

Release checklist

• ☐ The primary query is answered without an unsupported number.
• ☐ Every artifact and runtime is pinned.
• ☐ The representative task and failure policy are explicit.
• ☐ Lifecycle, cancellation, cleanup, and fallback are tested.
• ☐ User-content and network boundaries are documented.
• ☐ Result wording applies only to the recorded configuration.
• ☐ The page links to raw method or evidence when results are added.

Sources and related evidence

• ML Kit text recognition
• Cove benchmark method
• Google ML Kit text recognition v2
• Android CameraX image analysis
• Google ML Kit Android setup and model delivery

Chinese deployment and troubleshooting content is organized in the .奇连 AI 端侧专题.

Frequently asked questions