Handwriting recognition basics in Computer Vision - Model Pipeline Trace

Model Pipeline - Handwriting recognition basics

This pipeline takes images of handwritten digits and teaches a computer to recognize which digit is written. It cleans the images, extracts important features, trains a model to learn patterns, and then predicts digits from new images.

Data Flow - 5 Stages

1Input Data

70000 images x 28 x 28 pixels→Raw grayscale images of handwritten digits (0-9)→70000 images x 28 x 28 pixels

An image showing a handwritten '5' in grayscale

↓

2Preprocessing

70000 images x 28 x 28 pixels→Normalize pixel values from 0-255 to 0-1→70000 images x 28 x 28 pixels

Pixel value 150 becomes 0.588

↓

3Feature Engineering

70000 images x 28 x 28 pixels→Flatten each image into a 784-length vector→70000 samples x 784 features

28x28 image converted to [0.0, 0.1, ..., 0.9] vector

↓

4Model Training

70000 samples x 784 features→Train a neural network classifier to map features to digits→Trained model with 10 output classes

Model learns to predict digit '5' from input vector

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5Prediction

1 sample x 784 features→Model predicts digit class probabilities→1 sample x 10 classes

Output probabilities: [0.01, 0.02, ..., 0.85, ..., 0.01]

Training Trace - Epoch by Epoch

Loss
1.2 |*****
0.8 |****
0.5 |***
0.35|**
0.25|*
     +---------
     Epochs 1-5

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.60	Model starts learning basic digit patterns
2	0.8	0.75	Accuracy improves as model adjusts weights
3	0.5	0.85	Model captures more complex features
4	0.35	0.90	Loss decreases steadily, accuracy rises
5	0.25	0.93	Model converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Input Image Flattening

Layer 2: Hidden Layer with ReLU activation

Layer 3: Output Layer with Softmax

Model Quiz - 3 Questions

What does the preprocessing step do to the image data?

A Converts images to color

B Changes pixel values to a 0-1 scale

C Removes half of the pixels

D Adds noise to images

Key Insight

This visualization shows how a simple neural network learns to recognize handwritten digits by gradually improving predictions through training. Normalizing data and flattening images help the model understand patterns, while the softmax layer turns outputs into clear probabilities.