added machine learning project that detects handwritten texts

Machine_Learning/Handwritten Text Recognition Model/README.md

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+
+# Handwritten Text Recognition Model
+
+This code creates a Handwritten Text Recognition app using Machine Learning.
+
+The app provides two main functionalities
+
+1. Draw: Users can draw on a canvas, and the app will detect and display any text from the drawing.
+2. Upload Image: Users can upload an image, and the app will detect and display any text present in the uploaded image.
+
+
+## Screenshots
+
+![App Screenshot](results/OP5.png)
+![App Screenshot](results/OP7.png)
+![App Screenshot](results/OP2.png)
+
+
+
+
+## Tech Stack
+
+**Languages:** Python
+
+**Libraries:** easyocr, opencv, numpy, scipy etc
+
+**Framework:** Streamlit
+
+
+## Run Locally
+
+Install dependencies
+
+```bash
+  pip install -r requirements.txt
+```
+
+Start the server
+
+```bash
+  streamlit run app.py
+```
+
+

Machine_Learning/Handwritten Text Recognition Model/app.py

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+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+import cv2
+import easyocr
+import numpy as np
+from PIL import Image
+import io
+
+def detect_text(image):
+    reader = easyocr.Reader(['en'], gpu=False)
+    text_ = reader.readtext(np.array(image))
+
+    img = np.array(image)
+
+    for t_, t in enumerate(text_):
+        bbox, text, score = t
+        cv2.rectangle(img, tuple(map(int, bbox[0])), tuple(map(int, bbox[2])), (0, 255, 0), 2)
+        cv2.putText(img, text, tuple(map(int, bbox[0])), cv2.FONT_HERSHEY_TRIPLEX, 0.75, (255, 0, 0), 1)
+
+    return img, text_
+
+st.title("HandWritten Text Recognition")
+
+tab1, tab2 = st.tabs(["Draw", "Upload Image"])
+
+with tab1:
+    # Create a canvas component
+    canvas_result = st_canvas(
+        stroke_width=3,
+        stroke_color="#000000",
+        background_color="#ffffff",
+        height=400,
+        width=600,
+        drawing_mode="freedraw",
+        key="canvas",
+    )
+
+    # Detect text on drawn image
+    if st.button("Detect"):
+        if canvas_result.image_data is not None:
+            img = Image.fromarray(canvas_result.image_data.astype('uint8'), 'RGBA')
+            img = img.convert('RGB')
+            st.image(img, caption='Drawn Image.', use_column_width=True)
+
+            st.write("Processing the image...")
+            with st.spinner('Detecting text...'):
+                processed_image, text_ = detect_text(np.array(img))
+
+            st.image(processed_image, caption='Processed Image.', use_column_width=True)
+
+            st.write("Detected Text:")
+            for _, text, score in text_:
+                st.write(f"Text: **{text}** ")
+            processed_image_pil = Image.fromarray(processed_image)
+            buf = io.BytesIO()
+            processed_image_pil.save(buf, format="PNG")
+
+        else:
+            st.write("Please draw something on the canvas first.")
+
+with tab2:
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+        if st.button("Detect Text"):
+            st.write("Processing the image...")
+            with st.spinner('Detecting text...'):
+                processed_image, text_ = detect_text(np.array(image))
+
+            st.image(processed_image, caption='Processed Image.', use_column_width=True)
+
+            st.write("Detected Text:")
+            for _, text, score in text_:
+                st.write(f"Text: **{text}** ")
+
+            processed_image_pil = Image.fromarray(processed_image)
+            buf = io.BytesIO()
+            processed_image_pil.save(buf, format="PNG")
+

Machine_Learning/Handwritten Text Recognition Model/requirements.txt

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+easyocr==1.7.1
+googletrans==4.0.0rc1
+imutils==0.5.4
+langdetect==1.0.9
+numpy==1.24.4
+opencv_contrib_python==4.10.0.84
+opencv_python==4.10.0.82
+opencv_python_headless==4.10.0.84
+Pillow==10.4.0
+scipy==1.11.4
+streamlit==1.36.0
+streamlit_drawable_canvas==0.9.3