Open benwbrum opened 6 years ago
When we do the resizing part (430, 250) from the original image of size (3000+, 2000+), we are distorting the image. When we do the classification using CNN, there should be some distinct features that differentiate among the different classes. In our problem, the images are full of text and artifacts. So without correctly interpreting our trained model, how can we assure that it is correctly extracting the distinct features? For instance, we use Grad-CAM to localize the distinct feature.
We have two options,
Part of the challenges of presenting images for users to transcribe is knowing which images contain meaningful text (census entries) and which are irrelevant to our purposes (microfilm artifacts, district descriptions, total sheets, signatures). We would like software which can classify images which have entries from other images, whether accomplished through computer vision, fuzzy matching on OCR, or other methodologies.
The sample data presently includes
entries
orother
.