🚧 Do not hardcode used models

README.md

@@ -9,3 +9,17 @@ This offers a OCR-D compliant workspace processor for some of the functionality
 
 This processor only operates on the text line level and so needs a line segmentation (and by extension a binarized 
 image) as its input.
+
+Example Usage
+-------------
+
+~~~
+ocrd-calamari-recognize -p test-parameters.json -m mets.xml -I OCR-D-SEG-LINE -O OCR-D-OCR-CALAMARI
+~~~
+
+With `test-parameters.json`:
+~~~
+{
+    "checkpoint": "/path/to/some/trained/models/*.ckpt.json"
+}
+~~~

ocrd_calamari/ocrd-tool.json

@@ -18,7 +18,7 @@
         "OCR-D-OCR-CALAMARI"
       ],
       "parameters": {
-        "XXX checkpoint": {"type": "string", "format": "file", "cacheable": true},
+        "checkpoint": {"type": "string", "format": "file", "cacheable": true},
         "voter": {"type": "string", "default": "confidence_voter_default_ctc"}
       }
     }

ocrd_calamari/recognize.py

@@ -27,7 +27,7 @@ class CalamariRecognize(Processor):
     def _init_calamari(self):
         os.environ['TF_CPP_MIN_LOG_LEVEL'] = TF_CPP_MIN_LOG_LEVEL
 
-        checkpoints = glob('/home/mike/devel/experiments/train-calamari-gt4histocr/models/*.ckpt.json')  # XXX
+        checkpoints = glob(self.parameter['checkpoint'])
         self.predictor = MultiPredictor(checkpoints=checkpoints)
 
         voter_params = VoterParams()