Holistic handwritten word recognition using temporal features derived from off-line images

doi:10.1016/0167-8655(95)00125-5

Pattern Recognition Letters

Volume 17, Issue 5, 1 May 1996, Pages 537-540

https://doi.org/10.1016/0167-8655(95)00125-5 Get rights and content

Abstract

This paper describes an algorithm for holistic recognition of off-line cursive words using temporal stroke information derived from off-line script. Temporal information is extracted by traversing the strokes without explicit segmentation of the word into constituent characters. The word image is then mapped on to a feature vector matrix of uptrends and downtrends of strokes. This feature vector matrix is compared to prestored feature vector of lexicon entries and ranked accordingly. On a test set of images, the temporal feature extraction rate is 80%. Given the correct set of temporal features, the recognition rate of the holistic classifier is 81% on small lexicons.

References (9)

J. Camillerapp et al.
Off-line and on-line methods for cursive handwriting recognition
D.S. Doerman et al.
Recovery of temporal information from static images of handwriting
Internat. J. Computer Vision
(1995)
R.F.H. Farag
Word-level recognition of cursive script
IEEE Trans. Comput.
(1979)
V. Govindaraju et al.
Using temporal information in off-line word recognition

There are more references available in the full text version of this article.

Cited by (24)

Directed connection measurement for evaluating reconstructed stroke sequence in handwriting images
2005, Pattern Recognition
Citation Excerpt :
A very good review can be found in Ref. [1]. Govindaraju and Krishnamurthy [2] suggested that although each person has his/her own personal handwriting style, the basic pen-tip movements for two different persons writing the same script are close. Besides, Plamondon and Srihari [1] stated that the recovered temporal information may be helpful in improving the recognition performance.
Generally speaking, the performance of on-line handwriting recognition systems is better than that of off-line handwriting recognition systems. Many researchers then started to extract/estimate on-line/temporal information from a static handwritten image to improve the performance of off-line recognition systems. More specifically, various algorithms have been developed to extract the stroke sequence, which is an important temporal information, from a static handwritten image in the last decade. However, to the best of our knowledge, there are no methods to evaluate the performance of these algorithms. In view of this limitation, this paper presents a directed connection measurement for evaluating the performance of these methods. The measurement is designed in terms of direction and connection. The direction measurement is based on the consecutive arrangements of the items, while the connection measurement is based on both the number of disconnections and the Feigin and Cohen model (FCM) in ranking analysis. We replace the Kendall's distance, the key metric component in FCM, by a new connection metric. We have also proved that the new direction and connection metrics satisfy all axioms for being a valid metric. The two correlations are combined to form a directed connection correlation, which is applicable in comparing the performance of different stroke sequence recovery algorithms.
Local reference lines for handwritten phrase recognition
1999, Pattern Recognition
Reference line information has been used for diverse purposes in handwriting research, including word case classification, OCR, and holistic word recognition. In this paper, we argue that the commonly used global reference lines are inadequate for many handwritten phrase recognition applications. Individual words may be written at different orientations or vertically displaced with respect to one another. A function used to approximate the implicit baseline will not be differentiable or even continuous at some points. We have presented the case for local reference lines and illustrate its successful use in a system that verifies street name phrases in a postal application.
Personality trait analysis by graphology technique using machine learning
2019, International Journal of Innovative Technology and Exploring Engineering
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
2018, Proceedings - International Conference on Pattern Recognition
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
2018, arXiv
Handwritten Bangla word recognition using negative refraction based shape transformation
2018, Journal of Intelligent and Fuzzy Systems

View all citing articles on Scopus

View full text

Holistic handwritten word recognition using temporal features derived from off-line images

Abstract

Off-line and on-line methods for cursive handwriting recognition

Recovery of temporal information from static images of handwriting

Internat. J. Computer Vision

Word-level recognition of cursive script

IEEE Trans. Comput.

Using temporal information in off-line word recognition