Added python code and updated readme.md

README.md

@@ -1,2 +1,46 @@
 # Semantic-Emotification
-Semantic Emotification with OCR and NLTK
+## Semantic Emotification with OCR and NLTK
+
+This Python script utilizes EasyOCR, emoji, difflib, and OpenCV to perform Optical Character Recognition (OCR) on an image and then map detected words to a predefined list of emojis. The goal is to identify and display emojis corresponding to the words found in the image.
+
+## Prerequisites
+Before running the script, make sure you have the required Python libraries installed. You can install them using the following commands:
+
+```
+pip install easyocr emoji difflib matplotlib opencv-python-headless nltk
+```
+
+Additionally, download the NLTK data by running the following commands:
+
+```
+python -m nltk.downloader stopwords punkt
+```
+
+## Usage
+* Clone the repository or download the script:
+
+```
+git clone https://github.com/AgnivaMaiti/Semantic-Emotification.git
+cd emoji-ocr
+```
+
+* Place the image you want to analyze in the same directory as the script.
+
+* Open the script in a text editor and modify the image_path variable to point to your image file.
+
+```
+image_path = 'your_image.jpg'
+```
+
+* Run the script:
+
+```
+python emoji_ocr.py
+```
+
+## Output
+The script will print the detected text, preprocessed text, closest keywords, and corresponding emojis. If OCR does not detect any text in the image, a message stating so will be displayed.
+
+## Notes
+* The script uses a small list of predefined keywords for emoji mapping. You can expand the keywords list with more emojis according to your needs.
+* Ensure that the image is clear and contains text for accurate OCR results.

semantic-emotification.py

@@ -0,0 +1,65 @@
+import easyocr
+import emoji
+import difflib
+import matplotlib.pyplot as plt
+import cv2
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+
+nltk.download('stopwords')
+nltk.download('punkt')
+
+keywords = [
+    "grinning_face", "grinning_face_with_smiling_eyes", "grinning_face_with_big_eyes",
+    "pick", "grinning_squinting_face", "grinning_face_with_sweat",
+    "rolling_on_the_floor_laughing", "guitar", "slightly_smiling_face",
+    "cloud_with_lightning", "camera", "fog", "wind_face", "droplet",
+    "sweat_droplets", "ocean"]
+
+# I have used a very small list of keywords here, a full list of available emojis can be used instead.
+
+stop_words = set(stopwords.words('english'))
+
+def preprocess_text(text):
+    words = word_tokenize(text)
+    filtered_words = [word.lower() for word in words if word.lower() not in stop_words]
+    return filtered_words
+
+def find_closest_keywords(input_words):
+    closest_matches = [difflib.get_close_matches(word, keywords, n=1) for word in input_words]
+    closest_keywords = [match[0] if match else None for match in closest_matches]
+    return closest_keywords
+
+def get_emojis(keywords):
+    emojis = [emoji.emojize(f":{keyword}:") for keyword in keywords]
+    return [e for e in emojis if e != ':None:']
+
+def perform_ocr(image_path):
+    reader = easyocr.Reader(['en'])
+    image = cv2.imread(image_path)
+    results = reader.readtext(image)
+    detected_text = ' '.join([result[1] for result in results])
+    return detected_text
+
+def main():
+    print("Available keywords: {}".format(", ".join(keywords)))
+
+    image_path = r'C:\Users\KIIT\Downloads\WhatsApp Image 2023-12-31 at 10.29.51_128afb29.jpg'
+
+    detected_text = perform_ocr(image_path)
+
+    if detected_text:
+        preprocessed_text = preprocess_text(detected_text)
+        closest_keywords = find_closest_keywords(preprocessed_text)
+        emojis = get_emojis(closest_keywords)
+
+        print(f"Detected Text: {detected_text}")
+        print(f"Preprocessed Text: {preprocessed_text}")
+        print(f"Closest Keywords: {closest_keywords}")
+        print("Emojis: {}".format(emojis))
+    else:
+        print("OCR did not detect any text in the image.")
+
+if __name__ == "__main__":
+    main()