diff --git a/fastembed/common/utils.py b/fastembed/common/utils.py
index fefa3238..a46269fc 100644
--- a/fastembed/common/utils.py
+++ b/fastembed/common/utils.py
@@ -4,6 +4,7 @@
 import tempfile
 import unicodedata
 from pathlib import Path
+from functools import lru_cache
 from itertools import islice
 from typing import Iterable, TypeVar
 
@@ -93,5 +94,33 @@ def get_all_punctuation() -> set[str]:
     )
 
 
+@lru_cache(maxsize=None)
+def get_all_marks() -> str:
+    """Return the combining marks (Unicode category M) as regex character class ranges.
+
+    Marks include Tamil and Devanagari vowel signs and Arabic harakat. The regex word class
+    does not match them, so a pattern that only keeps word characters splits words in these
+    scripts at every mark. Ranges keep the class short, which keeps matching fast.
+    """
+    ranges: list[str] = []
+    start = None
+    for i in range(sys.maxunicode + 2):
+        is_mark = i <= sys.maxunicode and unicodedata.category(chr(i)).startswith("M")
+        if is_mark and start is None:
+            start = i
+        elif not is_mark and start is not None:
+            ranges.append(f"{re.escape(chr(start))}-{re.escape(chr(i - 1))}")
+            start = None
+    return "".join(ranges)
+
+
+@lru_cache(maxsize=None)
+def _non_alphanumeric_pattern() -> re.Pattern[str]:
+    return re.compile(rf"[^\w\s{get_all_marks()}]")
+
+
 def remove_non_alphanumeric(text: str) -> str:
-    return re.sub(r"[^\w\s]", " ", text, flags=re.UNICODE)
+    # ASCII text has no combining marks, and the plain class is faster to match.
+    if text.isascii():
+        return re.sub(r"[^\w\s]", " ", text)
+    return _non_alphanumeric_pattern().sub(" ", text)
diff --git a/fastembed/sparse/utils/tokenizer.py b/fastembed/sparse/utils/tokenizer.py
index e2129083..42d450fc 100644
--- a/fastembed/sparse/utils/tokenizer.py
+++ b/fastembed/sparse/utils/tokenizer.py
@@ -1,12 +1,25 @@
 # This code is a modified copy of the `NLTKWordTokenizer` class from `NLTK` library.
 
 import re
+from functools import lru_cache
+
+from fastembed.common.utils import get_all_marks
+
+
+@lru_cache(maxsize=None)
+def _non_word_pattern() -> re.Pattern[str]:
+    return re.compile(rf"[^\w{get_all_marks()}]")
 
 
 class SimpleTokenizer:
     @staticmethod
     def tokenize(text: str) -> list[str]:
-        text = re.sub(r"[^\w]", " ", text.lower())
+        text = text.lower()
+        # ASCII text has no combining marks, and the plain class is faster to match.
+        if text.isascii():
+            text = re.sub(r"[^\w]", " ", text)
+        else:
+            text = _non_word_pattern().sub(" ", text)
         text = re.sub(r"\s+", " ", text)
 
         return text.strip().split()
diff --git a/tests/test_sparse_embeddings.py b/tests/test_sparse_embeddings.py
index 3aafbe32..1f5738c8 100644
--- a/tests/test_sparse_embeddings.py
+++ b/tests/test_sparse_embeddings.py
@@ -4,8 +4,10 @@
 import pytest
 import numpy as np
 
+from fastembed.common.utils import remove_non_alphanumeric
 from fastembed.sparse.bm25 import Bm25
 from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
+from fastembed.sparse.utils.tokenizer import SimpleTokenizer
 from tests.utils import delete_model_cache, is_manual_run, should_test_model
 
 
@@ -311,6 +313,35 @@ def test_disable_stemmer_behavior(disable_stemmer: bool) -> None:
     assert result == expected, f"Expected {expected}, but got {result}"
 
 
+def test_combining_marks_do_not_split_words() -> None:
+    # Tamil and Devanagari vowel signs and Arabic harakat are combining marks, which the regex
+    # word class does not match.
+    text = "தமிழ் மொழி, हिन्दी भाषा! ذَهَبَ الطَّالِبُ"
+    assert SimpleTokenizer.tokenize(text) == [
+        "தமிழ்",
+        "மொழி",
+        "हिन्दी",
+        "भाषा",
+        "ذَهَبَ",
+        "الطَّالِبُ",
+    ]
+    assert remove_non_alphanumeric("தமிழ், हिन्दी!") == "தமிழ்  हिन्दी "
+
+
+@pytest.mark.parametrize(
+    "language,text,expected",
+    [
+        ("arabic", "ذَهَبَ الطَّالِبُ إِلَى المَدْرَسَةِ", ["ذهب", "طالب", "الي", "مدرس"]),
+        ("tamil", "சென்னை தமிழ்நாட்டின் தலைநகரம் ஆகும்", ["சென்", "தமிழ்நாடு", "தலைநகரம்", "ஆக்"]),
+    ],
+    ids=["arabic", "tamil"],
+)
+def test_stem_words_with_combining_marks(language: str, text: str, expected: list[str]) -> None:
+    model = Bm25("Qdrant/bm25", language=language)
+    tokens = model.tokenizer.tokenize(remove_non_alphanumeric(text))
+    assert model._stem(tokens) == expected
+
+
 class _PolishStemmer:
     def stem_word(self, word: str) -> str:
         for suffix in ("ami", "ach"):