SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.

解释：

这个google街景下整理生成的一些含数字字母的图片集。可以通过openCV等训练。。。。。。对学习大数据视觉神经很有帮助。

数据地址：

http://ufldl.stanford.edu/housenumbers/