Recognition of handwritten digits is a challenging task, because the writers may possibly write with dissimilar styles, sizes, width and shapes. A probabilistic neural network for recognizing handwritten digits is proposed here. Normalization of the digits of varying sizes is done for getting better boundary descriptor properties. The different boundary descriptor features extracted for recogni...

A novel Bayesian classifier with smaller eigenvalues reset by threshold based on database is proposed in this paper. The threshold is used to substitute eigenvalues of scatter matrices which are smaller than the threshold to minimize the classification error rate with a given database, thus improving the performance of Bayesian classifier. Several experiments have shown its effectiveness. The e...

In this paper, in order to improve the accuracy of handwritten number recognition, a convolutional neural network based on noise reduction and stack auto coding is formed by combining denoising algorithm with algorithm. MNIST dataset preprocessed first, then layer are constructed, finally improved used train test dataset. Experimental data show that proposed method achieves low loss value effec...

In this paper a two-stage HMM-based method for recognizing handwritten numeral strings is extended to work with handwritten numeral strings of unknown length. We have proposed a Bayesian-based string length predictor (SLP) to estimate the number of digits in a string taking into account its width in pixels. The top 3 decisions of the SLP module are used to control the maximum number of levels t...

Understanding a block of handwritten text means mapping it into a semantic representation, We describe an approach to reading a I>lock of handwritten text when there arc certain loose constraints placed on the spatial layout and syntax 01 the tnt. Early recognition of primitives guides the location of syntactic components. A system to read handwritten postal addresses is described as an instanc...

motivated by the Convolutional Neural Networks about digit recognition and ImageNet deep neural network by Krizhevsky et al. [1], I did this project on Guqin notation recognition, which classified reduced characters with positioned 1-10 (一 -十) in handwritten Chinese characters and translated to other music recording scores. I built a four-layer convolutional neural network using adjusted CaffeN...