feat(training): add segmentation training pipeline and complete Part 6

- Add /segtrain endpoint to OCR service (ZIP upload, ketos.segtrain,
  backup rotation, in-process model reload)
- Add segtrainModel() to OcrClient and RestClientOcrClient (10-min timeout,
  X-Training-Token header)
- Add SegmentationTrainingExportService: PAGE XML export with polygon
  de-normalization and per-page PNG rendering via PDFBox
- Add GET /api/ocr/segmentation-training-data/export endpoint
- Make TranscriptionBlock.text nullable for segmentation-only blocks
  (V31 migration)
- Add Paraglide i18n translation keys for all training UI strings (de/en/es)
- Pass source prop from TranscriptionEditView to TranscriptionBlock

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ocr-service/main.py

@@ -278,6 +278,75 @@ async def train_model(
     return result
 
 
+@app.post("/segtrain")
+async def segtrain_model(
+    file: UploadFile,
+    x_training_token: str | None = Header(default=None),
+):
+    """Fine-tune the blla segmentation model with uploaded PAGE XML training data.
+
+    Accepts a ZIP archive containing .png/.xml (PAGE XML) training pairs exported
+    by the Java backend. Training mutates in-process model state — not safe
+    if the service is replicated.
+    """
+    _check_training_token(x_training_token)
+
+    if not _models_ready:
+        raise HTTPException(status_code=503, detail="Models not loaded yet")
+
+    zip_bytes = await file.read()
+
+    training_run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+    log = logging.LoggerAdapter(logger, {"training_run_id": training_run_id})
+    log.info("Starting segmentation training run %s", training_run_id)
+
+    blla_model_path = os.environ.get("BLLA_MODEL_PATH", "/app/models/blla.mlmodel")
+
+    def _run_segtrain() -> dict:
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            with zipfile.ZipFile(io.BytesIO(zip_bytes)) as zf:
+                for entry in zf.namelist():
+                    _validate_zip_entry(entry, tmp_dir)
+                zf.extractall(tmp_dir)
+
+            log.info("Extracted %d ZIP entries for segmentation training", len(os.listdir(tmp_dir)))
+
+            xml_files = glob.glob(os.path.join(tmp_dir, "*.xml"))
+            if not xml_files:
+                raise HTTPException(status_code=422, detail="No PAGE XML files found in ZIP")
+
+            log.info("Training on %d PAGE XML files", len(xml_files))
+            output_model_path = os.path.join(tmp_dir, "fine_tuned_blla.mlmodel")
+
+            from kraken import ketos
+            result = ketos.segtrain(
+                ground_truth=xml_files,
+                load=blla_model_path if os.path.exists(blla_model_path) else None,
+                output=output_model_path,
+                format_type="path",
+            )
+
+            epochs = getattr(result, "epochs", None) or 0
+            loss = getattr(result, "best_loss", None)
+            accuracy = getattr(result, "best_accuracy", None)
+
+            log.info("Segmentation training complete — epochs=%s loss=%s", epochs, loss)
+
+            if os.path.exists(blla_model_path):
+                timestamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+                backup_path = f"{blla_model_path}.{timestamp}.bak"
+                shutil.copy2(blla_model_path, backup_path)
+                _rotate_backups(blla_model_path, keep=3)
+
+            shutil.move(output_model_path, blla_model_path)
+            log.info("Replaced blla model at %s", blla_model_path)
+
+            return {"loss": loss, "accuracy": accuracy, "epochs": epochs}
+
+    result = await asyncio.to_thread(_run_segtrain)
+    return result
+
+
 async def _download_and_convert_pdf(url: str) -> list[Image.Image]:
     """Download a PDF from a presigned URL and convert each page to a PIL Image."""
     _validate_url(url)