General Architecture

• Published in: Markov Models for Handwriting Recognition pp. 9 - 17
• Main Authors: Plötz, Thomas, Fink, Gernot A.
• Format: Book Chapter
• Language: English
• Published: London Springer London 07.09.2011
• Series: SpringerBriefs in Computer Science
• Subjects: Feature extraction; Handwriting recognition [overview]; Model decoding; Pre-processing; Segmentation free recognition; Serialization; System architecture
• ISBN: 9781447121879; 1447121872
• ISSN: 2191-5768; 2191-5776
• DOI: 10.1007/978-1-4471-2188-6_2

As a first step of document understanding a digital image of the document to be analyzed or the trajectory of the pen used for writing needs to be captured. From this raw data the relevant document elements (e.g., text lines) need to be segmented. These are then subject to a number of pre-processing steps that aim at reducing the variability in the appearance of the writing by applying a sequence of normalization operations. In order to be processed by a handwriting recognition system based on Markov models, text-line images and raw pen trajectories are then converted into a sequential representation—which is quite straight-forward for online data but requires some “trick” in the offline case. Based on the serialized data representation features are computed that characterize the local appearance of the script. These are fed into a Markov-model based decoder that produces a hypothesis for the segmentation and classification of the analyzed portion of handwritten text—usually as a sequence of word or character hypotheses.