A hybrid approach for off-line cursive script recognition

Cursive script recognition is commonly based on finding letters within a word and recognizing them separately. The segmentation process is ambiguous and difficult. This paper presents a hybrid method which combines individual recognizers: segmentation-based and word-based, to cope with difficulties...

Full description

Saved in:
Bibliographic Details
Main Author: Monreal, Justin T.
Format: text
Language:English
Published: Animo Repository 1998
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/etd_masteral/2495
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=9333&context=etd_masteral
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
Language: English
Description
Summary:Cursive script recognition is commonly based on finding letters within a word and recognizing them separately. The segmentation process is ambiguous and difficult. This paper presents a hybrid method which combines individual recognizers: segmentation-based and word-based, to cope with difficulties in recognizing cursive script. Words are first segmented into smaller subimages. A neural network is used to identify possible letters among the group. Letter information is combined with word shape information to get word identity. Recognition results of individual and hybrid recognizers are presented. The hybrid recognizer is found to perform better than individual recognizers.