diff --git a/doctor/lib/ocr_utils.py b/doctor/lib/ocr_utils.py
index d116b2d..d34ca99 100644
--- a/doctor/lib/ocr_utils.py
+++ b/doctor/lib/ocr_utils.py
@@ -8,9 +8,12 @@
 from PIL import Image
 
 
-def deskew(obj) -> bool:
+def deskew(obj: dict) -> bool:
     """Remove skewed text from a page
 
+    CTM stands for current transformation matrix.
+    Pdf plumber has a method to calculate the angle of text which we use here
+
     Traditionally this is only seen in circular stamps which confuses the
     content, or in perpendicular text of the ninth circuit courts which also
     confuses the text.
@@ -25,7 +28,7 @@ def deskew(obj) -> bool:
     return True
 
 
-def get_page_text(page: pdfplumber.PDF.pages, strip_margin: bool):
+def get_page_text(page: pdfplumber.PDF.pages, strip_margin: bool) -> str:
     """Extract page text
 
     Using pdf plumber extract out the text of the document that is not
@@ -58,7 +61,7 @@ def get_page_text(page: pdfplumber.PDF.pages, strip_margin: bool):
     return doc_text
 
 
-def page_images(page):
+def page_images(page: pdfplumber.pdf.Page) -> bool:
     """Does the page have images of a certain size
 
     Meant to exclude images that might be lines
@@ -72,7 +75,7 @@ def page_images(page):
     return False
 
 
-def page_annotations(page):
+def page_annotations(page: pdfplumber.pdf.Page) -> bool:
     """Does the page have annotations which could contain text
 
     :param page: pdf plumber
@@ -86,7 +89,7 @@ def page_annotations(page):
     return False
 
 
-def find_average_char_width(block_data):
+def find_average_char_width(block_data: pd.Series) -> int:
     """Average character width for a block of text
 
     :param block_data:
@@ -96,50 +99,15 @@ def find_average_char_width(block_data):
     return (fd.width / fd.text.str.len()).mean()
 
 
-# def validate_ocr_text(row, img):
-#     """Review OCR results for low confidence and reprocess if necessary
-#
-#     :param row:
-#     :param img:
-#     :return:
-#     """
-#     # If low confidence in the margins of drop character as likely artifact
-#     if row["left"] < 370 and row["conf"] <= 40:
-#         row["text"] = " " * len(row["text"])
-#     # if very low confidence and small - reprocess word with OCR for single line
-#     # this will give us a better chance to get the word right or remove junk
-#     elif row["conf"] < 10 and len(row["text"]) >= 3:
-#         # Give us a buffer around the word to increase OCR-ability
-#         bbox = (
-#             row["left"] - 5,
-#             row["top"] - 3,
-#             row["left"] + row["width"] + 5,
-#             row["top"] + row["height"] + 3,
-#         )
-#         config = "f'-c preserve_interword_spaces=1x1 --psm 7 -l eng'"
-#         word_df = pd.DataFrame(
-#             pytesseract.image_to_data(
-#                 img.crop(bbox), config=config, output_type=Output.DICT
-#             )
-#         )
-#         # If new word above low confidence - use new word
-#         new_words = " ".join(
-#             word_df.loc[word_df["conf"] > 10, "text"].tolist()
-#         )
-#         if new_words:
-#             row["text"] = new_words
-#         else:
-#             # Otherwise identify unknown word/words with empty box
-#             row["text"] = "□" * len(row["text"])
-#     return row
-
-
-def validate_ocr_text(row, img):
-    """
+def validate_ocr_text(row: pd.Series, img: Image) -> pd.Series:
+    """Validate the OCR results
 
-    :param row:
-    :param img:
-    :return:
+    Take a look at our OCR and remove the bad results
+    and if possible reprocess words one by one
+
+    :param row: row of ocr results
+    :param img: Page image
+    :return: Updated row if necessary
     """
     if row["left"] < 370 and row["conf"] <= 40:
         row["text"] = " " * len(row["text"])
@@ -170,7 +138,7 @@ def validate_ocr_text(row, img):
 def add_newlines(row: pd.Series, state: dict) -> dict:
     """Add new linebreaks into the ocr'd page
 
-    identify where line breaks should be added
+    Identify where line breaks should be added
 
     :param row: the row of data from tesseract
     :param state: the location data used to decide where line breaks should be