Update README.md

README.md

@@ -17,16 +17,19 @@ To implement whether any part of the front foot is behind the bowling crease to
 three main approaches. Below show what the objectives need when using all the approaches.
 
 •	Collect data
+
 For the model training purpose, it is required to collect the data. In this individual component two of the following scenarios 
 will be considered.
-                * Moment where any part of the front foot is behind the bowling crease.(legal ball)
-                * Moment where any part of the front foot is not behind the bowling crease.(no ball)
+                • Moment where any part of the front foot is behind the bowling crease.(legal ball)
+                • Moment where any part of the front foot is not behind the bowling crease.(no ball)
     
 •	Preprocessing of the data set
+
 Collect the footages of the players during their practice session and extract the frame.
 Refine the images and organize them into two different folders one as no ball and other as legal ball.
 
 •	Model training and testing
+
 To detect whether a ball is a no ball or a legal ball using machine learning, do a comparison 
 between Convolutional Neural Network (CNN) model and pretrained model MobileNet, ResNet.
 It can experiment with all three architectures and evaluate their performance on the dataset to select