Computer Vision License Validation

Business problem: Id verification system(valid or invalid) say driving license as id. How do we go about solving this business problem using Deep learning

Input - License Id Images

Approach

• Feature Definition
• Defining Elements
• Historical data
• Labeling / Annotation

Vision

• Problem #1 - Extract Face images
• Problem #2 - OCR, License Id, Dates, LicenseNumber, Authority
• Problem #3 - Detection for Signature Extracting
• Data Validation - Blurriness - Image Sharpening / Laplacian / Sobel / Canny edge to sharpen images. Non-readable - Far / Validation - Near View

Backend Validation

• API call
• Face Match
• Similarity Score
• Output - Valid License

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CNN Experiments - Solutions - Building End to End Solutions

CNN Experiments - Solutions - Building End-to-End Solutions

CNN Experiments

• Minimum Exp Without Aug
• Data Aug + CNN Model 
• Data Aug + CNN Model (Deeper Layers) - Few more convolution blocks
• Data Aug + CNN Model (Deeper Layers) - Few more convolution blocks + (Dropouts / Regularizer / Adjusting Learning rate)

To Launch a Product / Build Model things to consider

• Pre-requisites
• Data Collection
• Data Pre-processing and transformation
• Data Imbalances / Data Augmentation 
• Modelling
• Deployment
• Monitoring
• Real-time data training
• Collaborate with Healthcare prof
• Keep updating the model

We have 95% Accuracy, Remaining 5% how do we handle

• Similarity scores
• Ensemble methods
• Human in loop

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Can ML Solve this Problem ? Vision Problem - How to approach Damage Detection in Mobile Phones ?

How do you approach Damage Detection in Mobile Phones? 

Detecting defects on phones during exchange

Question - Can it be done with ML? 

• Student Answers - DL Vision

Question - Data Prerequisites?

Student Answers

• Physical damage to vision
• Images of the phone from various angles
• Software issues
• System diagnostics
• Images of cracked screens

Question - Model building

Student Answers

• Cnn classification 2 classes
• Damaged, not damaged
• Multiclass - damaged, degrees of damage (so that can identify price negotiation)
• inside parts, maybe images of phone when it is not damaged?

Real-world Way of Solving 

My Recommendation

• Detect Type of Phone, - Flip / Smart Phone
• Brand Detection (OCR)
• Image Similarity (Good Screen vs Similarity score to what you have)
• Line Detection - Count Cracks on Screen
• Segmentation to detect %% of cracked area
• Measure the deformation in the picture
• Yes / NO - Cracks
• Low / Medium / High
• Centre, Lower, Top

This is not a single model for all needs. This has to be based on brands, models, categories, Defect types, Data Collection, Labelling and Phased Adoption.

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