We had a business problem that required processing handwritten text.
Our off-the-shelf software only provided 48% accuracy because it was not
tailored to our specific problem.
We knew we could enhance the off-the-shelf solution.
First, we defined three steps.
Second, we described the text in computer language.
We started with line segmentation and leveraged neural networks
(also called deep learning) and image processing to analyze geometric
features and extract lines of text from the whole document.
Third, with clear and differentiated lines of text, a neural network
architecture provided probabilities for the presence of characters in
each line.
Finally, we arranged these characters into words to form meaningful
sentences by using an optimization engine.
By carefully monitoring performance over time and rigorously modifying
the approach as required, we continually improved to 93.6% accuracy.
With computer vision and more generally, advanced analytics, downloading
open-source tools is the right place to start, but, that is often when
the real work begins.