IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Parsing N-Best Lists of Handwritten Sentences
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Offline Grammar-Based Recognition of Handwritten Sentences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic scoring of short handwritten essays in reading comprehension tests
Artificial Intelligence
SwiftPost: a vision-based fast postal envelope identification system
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Ensemble methods to improve the performance of an English handwritten text line recognizer
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Automated scoring of handwritten essays based on latent semantic analysis
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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In this paper, we present a solution to the general vision problem of parsing and recognizing a set of correlated entities in the presence of imperfect information. Our solution mechanism involves the generation of multiple hypotheses in the initial stages of the system, and the use of very-large vocabulary recognition, together with a database of all the valid combinations of the correlated entities, to choose among the hypotheses. We have applied our ideas and techniques to the specific task of identifying the city, state and zip code fields in handwritten addresses. Given the image of a handwritten address, our algorithm produces a ranking of the 76,121-entry database of valid {city, state, zip} triples in the U.S, and in nearly 75% of the cases, the correct entry for the input address is assigned a rank of at most 10.