Character Recognition Using A Modular Spatiotemporal Connectionist Model

We describe a connectionist model for recognizing handprinted characters. Instead of treating the input as a static signal, the image is scanned over time and converted into a time-varying signal. The temporalized image is processed by a spatiotemporal connectionist network suitable for dealing with time-varying signals. The resulting system offers several attractive features, including shift-invariance and inherent retention of local spatial relationships along the temporalized axis, a reduction in the number of free parameters, and the ability to process images of arbitrary length. Connectionist networks were chosen as they offer learnability, rapid recognition, and attractive commercial possibilities. A modular and structured approach was taken in order to simplify network construction, optimization and analysis. Results on the task of handprinted digit recognition are among the best report to date on a set of real-world ZIP code digit images, provided by the United States Postal Service. The system achieved a 99.1% recognition rate on the training set and a 96.0% recognition rate on the test set with no rejections. A 99.0% recognition rate on the test set was achieved when 14.6% of the images were rejected

The effectiveness of features in pattern recognition

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On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples

Investigation of normalization techniques and their impact on a recognition rate in handwritten numeral recognition

This paper presents several normalization techniques used in handwritten numeral recognition and their impact on recognition rates. Experiments with five different feature vectors based on geometric invariants, Zernike moments and gradient features are conducted. The recognition rates obtained using combination of these methods with gradient features and the SVM-rbf classifier are comparable to the best state-of-art techniques

Feature Extraction Methods for Character Recognition

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