diff --git a/doctr/models/recognition/parseq/pytorch.py b/doctr/models/recognition/parseq/pytorch.py
index 25efc23252..294e0236b4 100644
--- a/doctr/models/recognition/parseq/pytorch.py
+++ b/doctr/models/recognition/parseq/pytorch.py
@@ -403,7 +403,9 @@ def __call__(
             for encoded_seq in out_idxs.cpu().numpy()
         ]
         # compute probabilties for each word up to the EOS token
-        probs = [preds_prob[i, : len(word)].clip(0, 1).mean().item() for i, word in enumerate(word_values)]
+        probs = [
+            preds_prob[i, : len(word)].clip(0, 1).mean().item() if word else 0.0 for i, word in enumerate(word_values)
+        ]
 
         return list(zip(word_values, probs))
 
diff --git a/doctr/models/recognition/parseq/tensorflow.py b/doctr/models/recognition/parseq/tensorflow.py
index 21a35605f5..28dc625cc9 100644
--- a/doctr/models/recognition/parseq/tensorflow.py
+++ b/doctr/models/recognition/parseq/tensorflow.py
@@ -432,7 +432,10 @@ def __call__(
         word_values = [word.decode() for word in decoded_strings_pred.numpy().tolist()]
 
         # compute probabilties for each word up to the EOS token
-        probs = [preds_prob[i, : len(word)].numpy().clip(0, 1).mean().item() for i, word in enumerate(word_values)]
+        probs = [
+            preds_prob[i, : len(word)].numpy().clip(0, 1).mean().item() if word else 0.0
+            for i, word in enumerate(word_values)
+        ]
 
         return list(zip(word_values, probs))
 
diff --git a/doctr/models/recognition/vitstr/pytorch.py b/doctr/models/recognition/vitstr/pytorch.py
index 0ff05f826a..6f68351d8d 100644
--- a/doctr/models/recognition/vitstr/pytorch.py
+++ b/doctr/models/recognition/vitstr/pytorch.py
@@ -167,7 +167,9 @@ def __call__(
             for encoded_seq in out_idxs.cpu().numpy()
         ]
         # compute probabilties for each word up to the EOS token
-        probs = [preds_prob[i, : len(word)].clip(0, 1).mean().item() for i, word in enumerate(word_values)]
+        probs = [
+            preds_prob[i, : len(word)].clip(0, 1).mean().item() if word else 0.0 for i, word in enumerate(word_values)
+        ]
 
         return list(zip(word_values, probs))
 
diff --git a/doctr/models/recognition/vitstr/tensorflow.py b/doctr/models/recognition/vitstr/tensorflow.py
index 70c7325b3f..117ba6a7aa 100644
--- a/doctr/models/recognition/vitstr/tensorflow.py
+++ b/doctr/models/recognition/vitstr/tensorflow.py
@@ -175,7 +175,10 @@ def __call__(
         word_values = [word.decode() for word in decoded_strings_pred.numpy().tolist()]
 
         # compute probabilties for each word up to the EOS token
-        probs = [preds_prob[i, : len(word)].numpy().clip(0, 1).mean().item() for i, word in enumerate(word_values)]
+        probs = [
+            preds_prob[i, : len(word)].numpy().clip(0, 1).mean().item() if word else 0.0
+            for i, word in enumerate(word_values)
+        ]
 
         return list(zip(word_values, probs))