Two-dimensional penalized signal regression for hand written digit recognition

Many attempts have been made to achieve successful recognition of handwritten digits. We report our results of using statistical method on handwritten digit recognition. A digitized handwritten numeral can be represented by an image with grayscales. The image includes features that are mapped into two-dimensional space with row and column coordinates. Based on this structure, two-dimensional penalized signal logistic regression (PSR) is applied to the recognition of handwritten digits. The data set is taken from the USPS zip code database that contains 7219 training images and 2007 test images. All the images have been deslanted and normalized into 16 x 16 pixels with various grayscales. The PSR method constructs a coefficient surface using a rich two-dimensional tensor product B-splines basis, so that the surface is more flexible than needed. We then penalize roughness of the coefficient surface with difference penalties on each coefficient associate with the rows and columns of the tensor product B-splines. The optimal penalty weight is found in several minutes of iterative operations. A competitive overall recognition error rate of 8.97% on the test data set was achieved. We will also review an artificial neural network approach for comparison. By using PSR, it requires neither long learning time nor large memory resources. Another advantage of the PSR method is that our results are obtained on the original USPS data set without any further image preprocessing. We also found that PSR algorithm was very capable to cope with high diversity and variation that were two major features of handwritten digits

Neural networks: The natural road to artificial iiitelligence

Despite the many advances of artificial intelligence (Al) technology, most notably in the area of expert systems, efforts to build intelligent systems that would approach the commonsense reasoning and sensory abilities of even a small child have not been rewarding. A small, but growing number of researches believe that the existing AI toolboxes of symbolic representation and heuristic search may not hold the answer, and that massively parallel networks of simple neuron-like processing elements may hold the key. In this overview article, we examine the use of neurally inspired concepts in the construction of intelligent machines, and address their practical applications, advantages, and limitations

Integration of traditional imaging, expert systems, and neural network techniques for enhanced recognition of handwritten information

Includes bibliographical references (p. 33-37).Research supported by the I.F.S.R.C. at M.I.T.Amar Gupta, John Riordan, Evelyn Roman

Real-time Online Chinese Character Recognition

In this project, I built a web application for handwritten Chinese characters recognition in real time. This system determines a Chinese character while a user is drawing/writing it. The techniques and steps I use to build the recognition system include data preparation, preprocessing, features extraction, and classification. To increase the accuracy, two different types of neural networks ared used in the system: a multi-layer neural network and a convolutional neural network

Old English Character Recognition Using Neural Networks

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning model, Artificial Neural Networks, to perform character recognition based on individual character images cropped directly from the images of the manuscript pages. We propose model dimensionality reduction methods that improve accuracy and computational effectiveness. Our experimental results show that the model we propose outperforms current automatic text recognition techniques

Handwritten character recognition using a gradient based feature extraction

Handwriting Recognition is the task of transforming a language that is represented in its spatial form of graphical marks into its symbolic representation. In Offline Handwriting Recognition, there are three steps: preprocessing of the image, segmentation of words into characters and recognition of the characters. In this thesis I implemented two methods for character recognition, which is the most important step in Offline Handwriting Recognition. The heart of character recognition is the features that are extracted from the character image. The accuracy of the classification of the character image depends on the quality of the features extracted from the image. The two methods presented in this thesis use two different types of features. One uses the connectivity features among various segments in a character image, and the other method uses the gradient feature at each pixel to construct the feature vectors. Both these methods are discussed in detail in the following chapters

A Neural Network-based Approach for the Machine Vision of Character Recognition

In this paper, an attempt is made to develop off-line recognition strategies for the isolated Handwritten English character(A to Z) and (0 to 9). Challenges in handwritten character recognition wholly lie in the variation and distortion of handwritten characters, since different people may use different style of handwritten, and direction to draw the same shape of the characters of their known script. The paper provides a review on the process of character recognition using neural network. Character recognition methods are listed under two main headlines. The Offline methods use the static images properties. The Offline methods are further divided into four methods, which are clustering, Feature Extraction, Pattern Matching and Artificial Neural Network. The Online methods are subdivided into k-NN classifier and direction based algorithm. Character preprocessing is used binarization, thresolding and segmentation method. Neural network based method improves the character recognition. The proposed method is based on the feed forward back propogation method to classify the characters. The ANN is trained using the Back Propogation algorithm. In the proposed system, English nue-merical letter is represented by binary numbers that are assume as input and fed to an ANN. Neural network followed by Back Propagation Algorithm which compromises Training