Recognition and verification of unconstrained handwritten words

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 27; no. 10; pp. 1509 - 1522
• Main Authors: Koerich, A.L., Sabourin, R., Suen, C.Y.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.10.2005; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Automatic Data Processing - methods; Boundaries; Character recognition; classifier combination; Computer Graphics; Computer science; control theory; systems; Connectionism. Neural networks; Delay; Documentation; Exact sciences and technology; Handwriting; Handwriting recognition; Hypotheses; Image databases; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image recognition; Image segmentation; Index Terms- Word hypothesis rejection; Information Storage and Retrieval - methods; large vocabulary; Lists; Models, Statistical; Neural networks; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Reading; Recognition; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; Text recognition; User-Computer Interface; Vocabulary; Writing; Vocabulary; large vocabulary; Image recognition; Hierarchical classification; neural networks; Word hypothesis rejection; Pattern recognition; Neural network; Character recognition; Handwriting recognition; classifier combination; Manuscript character; Pattern analysis
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2005.207

This paper presents a novel approach for the verification of the word hypotheses generated by a large vocabulary, offline handwritten word recognition system. Given a word image, the recognition system produces a ranked list of the N-best recognition hypotheses consisting of text transcripts, segmentation boundaries of the word hypotheses into characters, and recognition scores. The verification consists of an estimation of the probability of each segment representing a known class of character. Then, character probabilities are combined to produce word confidence scores which are further integrated with the recognition scores produced by the recognition system. The N-best recognition hypothesis list is reranked based on such composite scores. In the end, rejection rules are invoked to either accept the best recognition hypothesis of such a list or to reject the input word image. The use of the verification approach has improved the word recognition rate as well as the reliability of the recognition system, while not causing significant delays in the recognition process. Our approach is described in detail and the experimental results on a large database of unconstrained handwritten words extracted from postal envelopes are presented.