Comparison of ocr algorithms using fourier and wavelet based feature extraction

Download
2011
Onak, Önder Nazım
A lot of research have been carried in the field of optical character recognition. Selection of a feature extraction scheme is probably the most important factor in achieving high recognition performance. Fourier and wavelet transforms are among the popular feature extraction techniques allowing rotation invariant recognition. The performance of a particular feature extraction technique depends on the used dataset and the classifier. Di erent feature types may need di erent types of classifiers. In this thesis Fourier and wavelet based features are compared in terms of classification accuracy. The influence of noise with di erent intensities is also analyzed. Character recognition system is implemented with Matlab. Isolated gray scale character image first transformed into one dimensional function. Then, set of features are extracted. The feature set are fed to a classifier. Two types of classifier were used, Nearest Neighbor and Linear Discriminant Function. The performance of each feature extraction and classification methods were tested on various rotated and scaled character images.

Suggestions

Parameter estimation for instantaneous spectral imaging
Öktem, Sevinç Figen; Davila, Joseph M (2014-05-04)
Spectral imaging is a fundamental diagnostic technique in physical sciences with widespread application. Conventionally, spectral imaging techniques rely on a scanning process, which renders them unsuitable for dynamic scenes. Here we study the problem of estimating the physical parameters of interest from the measurements of a non-scanning spectral imager based on a parametric model. This inverse problem, which can be viewed as a multi-frame deblurring problem, is formulated as a maximum a posteriori (MAP)...
Optimizing core signal processing functions on a superscalar SIMD architecture
Uslu, Çağrı; Bazlamaçcı, Cüneyt Fehmi; Department of Electrical and Electronics Engineering (2019)
Digital Signal Processing (DSP) is the basis of many technologies, such as Image Processing, Speech Recognition, Radars, etc. Use of electronic devices such as smart- phones, smartwatches, self-driving cars and autonomous robots that take advantage of these technologies becomes widespread and hence it is more critical than ever for these technologies to be realized with high efficiency on cheaper and less power- hungry devices. Cortex-A15 processor architecture is one of the solutions from ARM to this requi...
New method for the fusion of complementary information from infrared and visual images for object detection
Ulusoy, İlkay (Institution of Engineering and Technology (IET), 2011-02-01)
Visual and infrared cameras have complementary properties and using them together may increase the performance of object detection applications. Although the fusion of visual and infrared information results in a better recall rate than using only one of those domains, there is always a decrease in the precision rate whereas the infrared domain on its own always has higher precision. Thus, the fusion of these domains is meaningful only for a better recall rate, which means that more foreground pixels are de...
Extraction of shape skeletons from grayscale images
Tarı, Zehra Sibel; Pien, H (Elsevier BV, 1997-05-01)
Shape skeletons have been used in computer vision to represent shapes and discover their salient features. Earlier attempts were based on morphological approach in which a shape is eroded successively and uniformly until it is reduced to its skeleton. The main difficulty with this approach is its sensitivity to noise and several approaches have been proposed for dealing with this problem. In this paper, we propose a new method based on diffusion to smooth out the noise and extract shape skeletons in a robus...
Convolutional neural networks analysed via inverse problem theory and sparse representations
Tarhan, Cem; Akar, Gözde (Institution of Engineering and Technology (IET), 2019-04-01)
Inverse problems in imaging such as denoising, deblurring, superresolution have been addressed for many decades. In recent years, convolutional neural networks (CNNs) have been widely used for many inverse problem areas. Although their indisputable success, CNNs are not mathematically validated as to how and what they learn. In this study, the authors prove that during training, CNN elements solve for inverse problems which are optimum solutions stored as CNN neuron filters. They discuss the necessity of mu...
Citation Formats
Ö. N. Onak, “Comparison of ocr algorithms using fourier and wavelet based feature extraction,” M.S. - Master of Science, Middle East Technical University, 2011.