DocumentCode :
3161004
Title :
A system for the off-line recognition of handwritten text
Author :
Breuel, Thomas M.
Author_Institution :
IDIAP, Martigny, Switzerland
Volume :
2
fYear :
1994
fDate :
9-13 Oct 1994
Firstpage :
129
Abstract :
A new system for the recognition of handwritten text is described. The system goes from raw, binary scanned images of census forms to ASCII transcriptions of the fields contained within the forms. The first step is to locate and extract the handwritten input from the forms. Then, a large number of character subimages are extracted and individual classified using a MLP (multilayer perceptron). A Viterbi-like algorithm is used to assemble the individual classified character subimages into optimal interpretations of an input string, taking into account both the quality of the overall segmentation and the degree to which each character subimage of the segmentation matches a character model. The system uses two different statistical language models, one based on a phrase dictionary and the other based on a simple word grammar. Hypotheses from recognition based on each language model are integrated using a decision tree classifier. Results from the application of the system to the recognition of handwritten responses on US census forms are reported
Keywords :
optical character recognition; ASCII transcriptions; US census forms; Viterbi-like algorithm; character subimages; decision tree classifier; handwritten text; multilayer perceptron; off-line recognition; phrase dictionary; raw binary scanned images; segmentation; simple word grammar; statistical language models; Assembly; Classification tree analysis; Computer vision; Decision trees; Dictionaries; Handwriting recognition; Image segmentation; Multilayer perceptrons; Postal services; Text recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on
Conference_Location :
Jerusalem
Print_ISBN :
0-8186-6270-0
Type :
conf
DOI :
10.1109/ICPR.1994.576889
Filename :
576889
Link To Document :
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