Automatic segmentation of the IAM off-line database for handwritten English text

This paper presents an automatic segmentation scheme for cursive handwritten text lines using the transcriptions of the text lines and a hidden Markov model (HMM) based recognition system. The segmentation scheme has been developed and tested on the IAM database that contains off-line images of cursively handwritten English text. The original version of this database contains ground truth for complete lines of text only, but not for individual words. With the method described in this paper the usability of the database is greatly improved because accurate bounding box information and ground truth for individual words (including punctuation characters) is now available as well. Applying the segmentation scheme on 417 pages of handwritten text a correct word segmentation rate of 98% has been achieved, producing correct bounding boxes for over 25'000 handwritten words.