Artificial Intelligence on Telegram: “✅ Real-World AI Project 2: Handwritten Digit Rec…”

✅ Real-World AI Project 2: Handwritten Digit Recognizer 🔢

This project focuses on image classification using deep learning. It introduces computer vision fundamentals with clear results.

Project Overview
- System predicts digits from 0 to 9
- Input is a grayscale image
- Output is a single digit class

Core concepts involved:
Image preprocessing
Convolutional Neural Networks
Feature extraction with filters
Softmax classification

Dataset
MNIST handwritten digits
60,000 training images
10,000 test images
Image size 28 × 28 pixels

Real-World Use Cases
Bank cheque processing
Postal code recognition
Exam sheet evaluation
Form digitization systems

Accuracy Reference
Basic CNN reaches around 98 percent on MNIST
Deeper CNN crosses 99 percent

Tools Used
Python
TensorFlow and Keras
NumPy
Matplotlib
Google Colab

Step 1. Import Libraries

import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt

Step 2. Load and Prepare Data

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = x_train.reshape(-1, 28, 28, 1)
x_test = x_test.reshape(-1, 28, 28, 1)

Step 3. Build CNN Model

model = models.Sequential([
  layers.Conv2D(32, (3,3), activation="relu", input_shape=(28,28,1)),
  layers.MaxPooling2D((2,2)),
  layers.Conv2D(64, (3,3), activation="relu"),
  layers.MaxPooling2D((2,2)),
  layers.Flatten(),
  layers.Dense(128, activation="relu"),
  layers.Dense(10, activation="softmax")
])

Step 4. Compile Model

model.compile(
  optimizer="adam",
  loss="sparse_categorical_crossentropy",
  metrics=["accuracy"]
)

Step 5. Train Model

model.fit(
  x_train, y_train,
  epochs=5,
  validation_split=0.1
)

Step 6. Evaluate Model

test_loss, test_accuracy = model.evaluate(x_test, y_test)
print("Test accuracy:", test_accuracy)

Expected output
Test accuracy around 0.98
Stable validation curve
Fast training on CPU or GPU

Testing with Custom Image
Convert image to grayscale
Resize to 28 × 28
Normalize pixel values
Pass through model.predict

Common Mistakes
Skipping normalization
Wrong image shape
Using RGB instead of grayscale

Portfolio Value
- Shows computer vision basics
- Demonstrates CNN understanding
- Easy to explain in interviews
- Strong beginner-to-intermediate project

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