diff --git a/evadb/functions/function_bootstrap_queries.py b/evadb/functions/function_bootstrap_queries.py
index f8186d4dd..70e2e8c89 100644
--- a/evadb/functions/function_bootstrap_queries.py
+++ b/evadb/functions/function_bootstrap_queries.py
@@ -176,6 +176,12 @@
     EvaDB_INSTALLATION_DIR
 )
 
+Text_recognition_query = """CREATE FUNCTION IF NOT EXISTS TextRecognizer
+        IMPL  '{}/functions/text_recognition.py';
+        """.format(
+    EvaDB_INSTALLATION_DIR
+)
+
 Text_feat_function_query = """CREATE FUNCTION IF NOT EXISTS SentenceFeatureExtractor
         IMPL  '{}/functions/sentence_feature_extractor.py';
         """.format(
@@ -282,6 +288,7 @@ def init_builtin_functions(db: EvaDBDatabase, mode: str = "debug") -> None:
         face_detection_function_query,
         # Mvit_function_query,
         Sift_function_query,
+        Text_recognition_query,
         Yolo_function_query,
         stablediffusion_function_query,
         dalle_function_query,
diff --git a/evadb/functions/text_recognition.py b/evadb/functions/text_recognition.py
new file mode 100644
index 000000000..3158d535a
--- /dev/null
+++ b/evadb/functions/text_recognition.py
@@ -0,0 +1,101 @@
+import concurrent.futures
+import pandas as pd
+import time
+from evadb.catalog.catalog_type import NdArrayType
+from evadb.functions.abstract.abstract_function import AbstractFunction
+from evadb.functions.decorators.decorators import forward, setup
+from evadb.functions.decorators.io_descriptors.data_types import PandasDataframe
+
+import easyocr
+from tqdm import tqdm
+
+
+reader = easyocr.Reader(["en"], gpu=True)
+
+
+def extract_text(image_path):
+    try:
+        extracted_text = ""
+        result = reader.readtext(image_path, detail=0)
+            for element in result:
+                extracted_text += element + " "
+        return extracted_text
+
+    except Exception as e:
+        print(f"Error for {image_path}: {str(e)}")
+        return str(e)
+
+def get_text_from_image(path):
+    try:
+        # Extract text using EasyOCR
+        extracted_text = extract_text(path)
+    except Exception as e:
+        error_msg = f"Error extracting text from {path}: {str(e)}"
+        print(error_msg)
+        return error_msg
+    return extracted_text
+
+
+class WebPageTextExtractor(AbstractFunction):
+    """
+    Arguments:
+        None
+
+    Input Signatures:
+        image_path (str) : The path to image from which to extract text.
+
+    Output Signatures:
+        extracted_text (list) : A list of text extracted from the provided image.
+
+    Example Usage:
+        You can use this function to extract text from an image
+    """
+
+    @property
+    def name(self) -> str:
+        return "ImageTextRecognizer"
+
+    @setup(cacheable=False, function_type="text-recognition")
+    def setup(self) -> None:
+        pass
+
+    @forward(
+        input_signatures=[
+            PandasDataframe(
+                columns=["image_path"],
+                column_types=[NdArrayType.STR],
+                column_shapes=[(None,)],
+            )
+        ],
+        output_signatures=[
+            PandasDataframe(
+                columns=["extracted_text"],
+                column_types=[NdArrayType.STR],
+                column_shapes=[(None,)],
+            )
+        ],
+    )
+    def forward(self, input_df):
+        if input_df.empty or input_df.iloc[0] is None:
+            raise ValueError("A path to image must be provided.")
+
+        print(input_df)
+
+        paths = input_df["path"]
+
+        num_workers = 1
+
+        start = time.time()
+        extracted_text_lists = []
+        # Use ThreadPoolExecutor for concurrent processing
+        with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
+            # Submit tasks to extract text from each URL
+            extracted_text_lists = list(
+                tqdm(executor.map(get_text_from_image, paths), total=len(paths))
+            )
+
+        # Create a DataFrame from the extracted text
+        extracted_text_df = pd.DataFrame({"extracted_text": extracted_text_lists})
+        end = time.time()
+        print("time taken: {:.2f}s".format(end - start))
+        return extracted_text_df