At this point, coverage was still below 90 percent. The team instrument the training and test set performance to determine whether the problem was underﬁtting or overﬁtting. Because training and test set error were so similar, this suggested that the problem was due to either underﬁtting or a problem with the training data. Then, they found input image had been cropped too tightly, which remove some digits of address. So, they expand the width of the crop region, which added 10% to the transcription systems coverage.
Finally, the last few percentage points of the performance came from adjusting hyperparameters.



State University of New York at Stony Brook

Take the Street View transcription system as an example. The process began with data collection. The cars collected the raw data, and human operators provided labels.


Example: Multi-Digit Number Recognition

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

Deep Learning

The transcription project began with a choice of performance metrics and desired values for these metrics. The goal was to obtain human-level, 98 percent accuracy. The next step is to rapidly establish a sensible baseline system. The team chose a convolutional network with rectiﬁed linear units, which is good for a vision tasks. Then iteratively reﬁne the baseline and test whether each change makes an improvement.



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