Kraken offers "multi-script" (actually multi-model) prediction in one pass, so instead of a fixed model, we could run with multiple models and use the annotated language and script mappings to select per-segment (as in ocrd-tesserocr-recognize with xpath_model).
IIUC, that would entail using mm_rpred (instead of rpred) and passing lang/script to bounds['boxes'][...]['tags'] (or bounds['lines'][...]['tags'] with baseline segmentation) and a dict from lang/script to model names as the first arg.
Kraken offers "multi-script" (actually multi-model) prediction in one pass, so instead of a fixed model, we could run with multiple models and use the annotated language and script mappings to select per-segment (as in ocrd-tesserocr-recognize with
xpath_model
).IIUC, that would entail using
mm_rpred
(instead ofrpred
) and passing lang/script tobounds['boxes'][...]['tags']
(orbounds['lines'][...]['tags']
with baseline segmentation) and a dict from lang/script to model names as the first arg.