Street-Level House Numbers Dataset – Street View House Numbers

Project Overview

The project focuses on leveraging the Street View House Numbers (SVHN) dataset to enhance the capabilities of computer vision systems, particularly for tasks like automatic number plate recognition and navigation assistance.

Objective

The goal is to gather a thorough collection of street-level house numbers taken from diverse urban and suburban settings. This dataset will be a valuable asset for training and evaluating machine learning algorithms, specifically those designed to accurately recognize and understand house numbers in real-life situations.

Scope

The dataset encompasses a wide range of urban and suburban landscapes, capturing diverse environmental conditions, lighting variations, and architectural styles to ensure robust model performance across different scenarios.

Sources

Street View Images: Utilizing Google Street View and other street imaging platforms, we collected a large volume of images containing street-level house numbers from different locations across India.
Manual Photography: In addition to automated data sources, we manually captured images of house numbers in various neighborhoods and cities to ensure a comprehensive dataset representation.

Data Collection Metrics

Total Data Collected: 50,000 high-resolution images containing street-level house numbers.
Data Annotated for ML Training: 60,000 images with meticulously labeled house numbers for machine learning training purposes.

Annotation Process

House Number Extraction: Each image was meticulously analyzed to extract and label individual house numbers, ensuring accurate identification and localization.

Annotation Metrics

Number of Annotations: Successfully annotated 60,000 images, encompassing a total of 120,000 individual house numbers

Quality Assurance

Dataset Validation: We conducted thorough validation procedures to confirm the accuracy and consistency of annotations throughout the dataset, ensuring its reliability and quality.
Privacy Protection: Stringent measures were adopted to anonymize sensitive data, prioritizing compliance with relevant data protection regulations and safeguarding individuals’ privacy.
Improvement Process: Feedback mechanisms were established to continuously refine the annotation process, aiming to enhance the overall quality and effectiveness of the dataset.

QA Metrics

Annotation Accuracy: Achieved a high annotation accuracy rate of 98% for house number identification and localization.
Privacy Compliance: Maintained full compliance with privacy regulations, ensuring the protection of individuals’ privacy rights.

Conclusion

The development of the Street View House Numbers (SVHN) dataset signifies a noteworthy progression in computer vision research. It offers a comprehensive and diverse collection suitable for training and assessing machine learning models designed for street-level house number recognition tasks. This dataset is poised to be instrumental in improving the precision and dependability of automated systems utilized in urban navigation, postal services, and various related applications.