Can a convolutional neural network classify text document images? I know convolutional neural networks are commonly used for image recognition, but I was wondering if they would be able to distinguish between predominantly text-based documents vs something like objects. For example, if you trained using images of the first page of invoices matched to a vendor name, could you get a CNN to predict the vendor based on an image? If not, is there a different AI technique better suited that is purely image-based, or would it require OCR and leveraging the text in the invoice?
Update: based on a comment, my ask my not be clear. I'm not trying to see if the CNN can differentiate between a document (mostly text based image) and a photo image. I want to know if based on a gif/jpeg/png of a document (no OCR performed) a CNN would be able to classify the documents, which basically could be used as a means of identifying the vendor.
Yes, it is possible, CNNs can also be used for OCR (see the MNIST task and this blog), although it's not the common way for OCR because it is considered a bit overkill and inefficient. Furthermore, OCR is considered a solved problem already and don't need deep learning to do well, unless perhaps under unfavorable conditions like dark lighting, complex background of the document, weird fonts, occlusion of text, etc.
Breaking down your questions:

a CNN can likely classify an image as &quot;this image has a word document in it&quot; vs &quot;this image does not have a word document in it&quot;. The word document itself, and the outline of the words, should form distinct enough features for the CNN to discriminate against
a CNN can likely identify the vendor based on the logo of the vendor if you have enough training images of it at various distortions (angles, occlusions, different lighting, etc)
a CNN can likely do OCR well enough to extract the text, and then an entity extraction model can identify the vendor from the words. However, this approach would be uncommon as OCR is already considered a solved problem. But a neural network can potentially help preprocessing difficult OCR images (rotating image, identifying text boundaries, fix lighting, perform super-resolution, etc) to increase OCR accuracy