Interactive training for handwriting recognition in historical document collections
Douglas J. Kennard, William A. Barrett · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
We present a method of interactive training for handwriting recognition in collections of documents. As the user transcribes (labels) the words in the training set, words are automatically skipped if they appear to match words that are already transcribed. By reducing the amount of redundant training, better coverage of the data is achieved, resulting in more accurate recognition. Using word-level features for training and recognition in a collection of George Washington's manuscripts, the recognition ratio is approximately 2%-8% higher after training with our interactive method than after training the same number of words sequentially. Using our approach, less training is required to achieve an equivalent recognition ratio. A slight improvement in recognition ratio is also observed when using our method on a second data set, which consists of several pages from a diary written by Jennie Leavitt Smith.