A hierarchical, HMM-based automatic evaluation of OCR accuracy for a digital library of books
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Old Handwritten Musical Symbol Classification by a Dynamic Time Warping Based Method
Graphics Recognition. Recent Advances and New Opportunities
A comprehensive evaluation methodology for noisy historical document recognition techniques
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
A novel two-level clustering method for time series data analysis
Expert Systems with Applications: An International Journal
User-assisted alignment of Arabic historical manuscripts
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Text line segmentation for gray scale historical document images
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Aligning transcripts to automatically segmented handwritten manuscripts
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Text line extraction for historical document images
Pattern Recognition Letters
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Today's digital libraries increasingly include not only printed text but also scanned handwritten pages and other multimedia material. There are, however, few tools available for manipulating handwritten pages. Here, we propose an algorithm based on dynamic time warping (DTW) for a word by word alignment of handwritten documents with their (ASCII) transcripts. We see at least three uses for such alignment algorithms. First, alignment algorithms allow us to produce displays (for example on the web) that allow a person to easily find their place in the manuscript when reading a transcript. Second, such alignment algorithms will allow us to produce large quantities of ground truth data for evaluating handwriting recognition algorithms.Third, such algorithms allow us to produce indices in a straightforward manner for handwriting material. We provide experimental results of our algorithm on a set of 70 pages of historical handwritten material - specifically the writings of George Washington. Our method achieves74.5% accuracy on line by line alignment and 60.5% accuracy when aligning whole pages at time.