DocReader: Bounding-Box Free Training of a Document Information Extraction Model
This addresses the annotation bottleneck in document processing for business applications, enabling automation without costly manual labeling, though it is incremental in leveraging existing data.
The paper tackles the problem of document information extraction by eliminating the need for bounding-box annotations during training, presenting DocReader, an end-to-end neural network that uses only images and target values. It demonstrates that DocReader can reach and surpass methods requiring bounding-boxes, offering a path for continual learning in production.
Information extraction from documents is a ubiquitous first step in many business applications. During this step, the entries of various fields must first be read from the images of scanned documents before being further processed and inserted into the corresponding databases. While many different methods have been developed over the past years in order to automate the above extraction step, they all share the requirement of bounding-box or text segment annotations of their training documents. In this work we present DocReader, an end-to-end neural-network-based information extraction solution which can be trained using solely the images and the target values that need to be read. The DocReader can thus leverage existing historical extraction data, completely eliminating the need for any additional annotations beyond what is naturally available in existing human-operated service centres. We demonstrate that the DocReader can reach and surpass other methods which require bounding-boxes for training, as well as provide a clear path for continual learning during its deployment in production.