show output

app.py

@@ -21,17 +21,25 @@ def analyse():
     frame = request.json.get('key')
     cv_frame = data_uri_to_image(frame)
     gray_image = cv2.cvtColor(cv_frame, cv2.COLOR_BGR2GRAY)
+    blur = cv2.bilateralFilter(gray_image,4,20,20)
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) # histograme of distributed pixel values
+    equalized = clahe.apply(blur)
 
 # Optional: Apply thresholding or other preprocessing techniques
 # _, thresh_image = cv2.threshold(gray_image, 150, 255, cv2.THRESH_BINARY)
 
 # Use pytesseract to extract text
-    results = reader.readtext(gray_image)
+    results = reader.readtext(equalized)
     results_array =[]
     for (bbox, text, prob) in results:
         results_array.append(f"Detected text: {text} (Confidence: {prob})")
 
 # Iterate through the results and format them
+    cv2.imshow("clahe", equalized)
+    cv2.imshow("blur", blur)
+    cv2.imshow("orig", gray_image)
+    cv2.waitKey(0)
+
     return str(results_array)
 
 

static/index.js

@@ -89,6 +89,12 @@ function send_frame(dataUrl) {
   .then(response => response.text())
   .then(data => {
       console.log(data)
+      var span = document.createElement('span');
+span.textContent = data;
+span.style.color = 'black'; 
+span.style.fontSize = '16px'; 
+document.body.appendChild(span);
+
   })
   .catch((error) => {
       console.error('Error:', error);