SVHNClassifier and SVHNClassifier-PyTorch

These are ecosystem siblings—parallel implementations of the same research paper in different deep learning frameworks (TensorFlow vs. PyTorch), allowing users to choose based on their preferred framework rather than requiring use of both together.

SVHNClassifier
49
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 23/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 21/25
Stars: 210
Forks: 72
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: GPL-3.0
Stars: 201
Forks: 44
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About SVHNClassifier

potterhsu/SVHNClassifier

A TensorFlow implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks (http://arxiv.org/pdf/1312.6082.pdf)

This helps automatically recognize multi-digit house numbers from street-level images. You provide an image containing house numbers, and it outputs the identified digits. This is useful for urban planners, mapping services, or researchers analyzing urban environments.

urban-planning image-analysis mapping-services street-data number-recognition

About SVHNClassifier-PyTorch

potterhsu/SVHNClassifier-PyTorch

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks (http://arxiv.org/pdf/1312.6082.pdf)

This project helps quickly and accurately identify multi-digit numbers found in street-level images, like house numbers or speed limits. You provide an image containing one or more digits, and it outputs the sequence of digits it detects. This is useful for anyone working with automated data collection from street imagery, such as city planners, mapping services, or urban data analysts.

street-image-analysis urban-data-capture number-recognition automated-mapping asset-tagging

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