Projects

6. Core Functions

• ocr_thread(): Runs OCR in separate threads.
• alerts(): Validates plate formats and filters duplicates.
• matchCount(): Avoids repeated alerts.
• add_data_to_csv(): Logs to CSV, uploads to S3, inserts to MongoDB.
• insert_data_anpr(): Checks DL, insurance, and vehicle registry.
• UpdateAlert(): Maintains alert audit trail.

7. Threaded OCR

OCR runs in separate threads per detection to avoid blocking video stream performance.

8. Security & Validation

• Regex ensures only valid Indian plate formats are logged.
• Tracks direction (IN/OUT) using Track IDs.
• Handles blurry, partial, or confusing plate formats gracefully.

9. Backend Integration

• AWS S3: Stores annotated images, accessible via URL.
• MongoDB: Stores alert metadata for analytics and filtering.
• Local CSV and JSON provide offline access and auditing support.

10. Use Cases

• Automated gate access for warehouses/factories.
• Timestamp-based vehicle tracking and alerting.

11. Highlights

• Regex boosts accuracy by ~20% by removing noise.
• Multithreading keeps processing real-time.
• Supports both 9- and 10-character plates.
• Prevents duplicate alerts using Track ID logic.

Data Warehouse System
The problem of this project is that we have to design a scalable data warehouse system that contains various categories and classified image in that categories with no duplication and various different operation. Trained the model on more than 100k images for our data warehouse system and perform different operation on these images. Integrate the whole system in the pipeline and create a GUI and API for the user interface. The accuracy of the system is 99.38 %.
Research paper used in AI-Model.
1. VGG-16 Architecture | Source: Very Deep Convolutional Networks for Large-Scale Image Recognition
2.Hashing Techniques | Source: Selecting a Hashing Algorithm
2. Simialrity Algorithm | Source: An Online Algorithm for Large Scale Image Similarity Learning

Recommender System
The problem of this project is that we have to design a recommender system for male & female based on their dressing style and face shape etc. For this project, we have used the detection and classification model. Trained the model on the huge number of label images for our detection and classification model and perform different operation on these detected images. Integrate the whole system in the pipeline and create a GUI and API for the user interface.
Research paper used in AI-Model.
1. Object Detection | Source: You Only Look Once: Unified, Real-Time Object Detection
2. Classification | Source: Rethinking the Inception Architecture for Computer Vision
3. Clustering | Source: Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance
4. Recommender System (using Bi-Directional LSTM) | Source: Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling

Monitor the Person Appearance
Problem Statement: Monitor the person’s attendance. Associate the faces with the existing employee database to identify each employee record the time stamp and date for each employee recognized.
Data-set : 100 hrs. Video footage for analysis and to build out model for monitor of person appearance.
Output: The output of the analysis will be recorded in an csv file, which will record the person’s id and the his/her time stamp and date
Research paper used in AI-Model.
1. Object Detection | Source: SSD: Single Shot MultiBox Detector
2. Face-Recognition | Source: FaceNet: A Unified Embedding for Face Recognition and Clustering
3. Face-Tracking | Source: An Algorithm For Centroid-Based Tracking of Moving Objects