  Recent work in word spotting in handwritten documents has yielded impressive
results. This progress has largely been made by supervised learning systems,
which are dependent on manually annotated data, making deployment to new
collections a significant effort. In this paper, we propose an approach that
utilises transcripts without bounding box annotations to train
segmentation-free query-by-string word spotting models, given a partially
trained model. This is done through a training-free alignment procedure based
on hidden Markov models. This procedure creates a tentative mapping between
word region proposals and the transcriptions to automatically create additional
weakly annotated training data, without choosing any single alignment
possibility as the correct one. When only using between 1% and 7% of the fully
annotated training sets for partial convergence, we automatically annotate the
remaining training data and successfully train using it. On all our datasets,
our final trained model then comes within a few mAP% of the performance from a
model trained with the full training set used as ground truth. We believe that
this will be a significant advance towards a more general use of word spotting,
since digital transcription data will already exist for parts of many
collections of interest.

Computer Vision and Pattern Recognition