diff --git a/wordcloud/tokenization.py b/wordcloud/tokenization.py
index <HASH>..<HASH> 100644
--- a/wordcloud/tokenization.py
+++ b/wordcloud/tokenization.py
@@ -40,7 +40,7 @@ def unigrams_and_bigrams(words, stopwords, normalize_plurals=True, collocation_t
     # We must create the bigrams before removing the stopword tokens from the words, or else we get bigrams like
     # "thank much" from "thank you very much".
     # We don't allow any of the words in the bigram to be stopwords
-    bigrams = list(p for p in pairwise(words) if not any(w in stopwords for w in p))
+    bigrams = list(p for p in pairwise(words) if not any(w.lower() in stopwords for w in p))
     n_words = len(words)
     counts_unigrams, standard_form = process_tokens(
         words, normalize_plurals=normalize_plurals)
@@ -67,7 +67,7 @@ def unigrams_and_bigrams(words, stopwords, normalize_plurals=True, collocation_t
             counts_unigrams[bigram_string] = counts_bigrams[bigram_string]
     for word, count in list(counts_unigrams.items()):
         # remove stopwords and empty / negative counts
-        if all(w in stopwords for w in word.split(" ")) or count <= 0:
+        if all(w.lower() in stopwords for w in word.split(" ")) or count <= 0:
             del counts_unigrams[word]
     return counts_unigrams
Update tokenization.py

 In function unigrams_and_bigrams, applied lower() to each word before checking whether it is a stopword