Add IAM paragraphs dataset

1 files changed, 7 insertions, 3 deletions

diff --git a/text_recognizer/data/emnist.py b/text_recognizer/data/emnist.py
index 3e10b5f..eda490a 100644
--- a/text_recognizer/data/emnist.py
+++ b/text_recognizer/data/emnist.py
@@ -1,6 +1,6 @@
 """EMNIST dataset: downloads it from FSDL aws url if not present."""
 from pathlib import Path
-from typing import Dict, List, Sequence, Tuple
+from typing import Dict, List, Optional, Sequence, Tuple
 import json
 import os
 import shutil
@@ -52,7 +52,7 @@ class EMNIST(BaseDataModule):
         self.data_val = None
         self.data_test = None
         self.transform = transforms.Compose([transforms.ToTensor()])
-        self.dims = (1, * self.input_shape)
+        self.dims = (1, *self.input_shape)
         self.output_dims = (1,)
 
     def prepare_data(self) -> None:
@@ -95,13 +95,17 @@ class EMNIST(BaseDataModule):
         return basic + data
 
 
-def emnist_mapping() -> Tuple[List, Dict[str, int], List[int]]:
+def emnist_mapping(
+    extra_symbols: Optional[List[str]],
+) -> Tuple[List, Dict[str, int], List[int]]:
     """Return the EMNIST mapping."""
     if not ESSENTIALS_FILENAME.exists():
         download_and_process_emnist()
     with ESSENTIALS_FILENAME.open() as f:
         essentials = json.load(f)
     mapping = list(essentials["characters"])
+    if extra_symbols is not None:
+        mapping += extra_symbols
     inverse_mapping = {v: k for k, v in enumerate(mapping)}
     input_shape = essentials["input_shape"]
     return mapping, inverse_mapping, input_shape