Learning Deep Representations for Word Spotting Under Weak Supervision
This addresses the annotation bottleneck for document analysis researchers, though it is incremental as it builds on existing CNN methods.
The paper tackles the problem of reducing manual annotation effort for word spotting in handwritten documents using CNNs, achieving results competitive with state-of-the-art methods while requiring shorter training times and less annotation.
Convolutional Neural Networks have made their mark in various fields of computer vision in recent years. They have achieved state-of-the-art performance in the field of document analysis as well. However, CNNs require a large amount of annotated training data and, hence, great manual effort. In our approach, we introduce a method to drastically reduce the manual annotation effort while retaining the high performance of a CNN for word spotting in handwritten documents. The model is learned with weak supervision using a combination of synthetically generated training data and a small subset of the training partition of the handwritten data set. We show that the network achieves results highly competitive to the state-of-the-art in word spotting with shorter training times and a fraction of the annotation effort.