Aligning Transcripts to Automatically Segmented Handwritten Manuscripts

Abstract. Training and evaluation of techniques for handwriting recognition and retrieval is a challenge given that it is difficult to create large ground-truthed datasets. This is especially true for historical handwritten datasets. In many instances the ground truth has to be created by manually transcribing each word, which is a very labor intensive process. Sometimes transcriptions are available for some manuscripts. These transcriptions were created for other purposes and hence correspondence at the word, line, or sentence level may not be available. To be useful for training and evaluation, a word level correspondence must be available between the segmented handwritten word images and the ASCII transcriptions. Creating this correspondence or alignment is challenging because the segmentation is often errorful and the ASCII transcription may also have errors in it. Very little work has been done on the alignment of handwritten data to transcripts. Here, a novel Hidden Markov Model ...

• Type: Conference
• Year: 2006
• Where: DAS
• Authors: Jamie L. Rothfeder, R. Manmatha, Toni M. Rath
• Keywords: Alignment Algorithm, Ascii Transcription, DAS 2006, Document Analysis, Historical Handwritten Datasets