Novel Algorithm For Recognition Research Articles

ScriptCalc: Handwriting Recognition with Mathematical Equation Solving

Abstract: The present paper proposes a novel algorithm for recognition of handwritten digits. For this, the present paper classified the digits into two groups: one group consists of blobs with/without stems and the other digits with stems only. The blobs are identified based on a new concept called morphological region filling methods. This eliminates the problem of finding the size of blobs and their structuring elements. The digits with blobs and stems are identified by a new concept called ‘connected component’. This method completely eliminates the complex process of recognition of horizontal or vertical lines and the property called ‘concavities’. The digits with only stems are recognized, by extending stems into blobs by using connected component approach of morphology. The present method has been applied and tested with various handwritten digits from modified NIST (National Institute of Standards and Technology) handwritten digit database (MNIST), and the success rate has been given. The present method is also compared with various existing methods. Once the digits are recognized, they are assembled back into their respective positions within the equation. Mathematical equation solving techniques are then applied to evaluate the expression and obtain the result

Finger vein recognition algorithm based on optimized GHT

In this paper, we present a novel algorithm for recognition of vein features based on optimized generalized Hough transform (GHT). The new algorithm involves several steps. First, it extracts singular points from the binary image of finger veins, and segments the finger veins by these points. Then it selects valid segments and sequences them by way of chain codes. Next it uses the optimized GHT to differentiate sectional curves of finger veins from the whole finger vein image. Using this approach reduces the influence of fragmentation, enhances adaptability for displacement, rotation, and zooming, and accordingly improves the quality of finger vein recognition. We have tested the proposed method with actual finger vein images and produced very satisfactory reassembly results.

Classification and recognition of handwritten digits by using mathematical morphology

The present paper proposes a novel algorithm for recognition of handwritten digits. For this, the present paper classified the digits into two groups: one group consists of blobs with/without stems and the other digits with stems only. The blobs are identified based on a new concept called morphological region filling methods. This eliminates the problem of finding the size of blobs and their structuring elements. The digits with blobs and stems are identified by a new concept called ‘connected component’. This method completely eliminates the complex process of recognition of horizontal or vertical lines and the property called ‘concavities’. The digits with only stems are recognized, by extending stems into blobs by using connected component approach of morphology. The present method has been applied and tested with various handwritten digits from modified NIST (National Institute of Standards and Technology) handwritten digit database (MNIST), and the success rate has been given. The present method is also compared with various existing methods.

Data compaction and vector scan e-beam system performance improvement using a novel algorithm for recognition of pattern step and repeats

Many classes of circuit pattern designs contain regular repeats of individual shapes. However, design manipulations (such as personalization), or electron-beam postprocessor functions (such as partitioning and dose assignment for proximity effect correction, overlap elimination, etc.) often destroy some of the pattern regularity, leaving smaller repetitive subpatterns. We describe a novel heuristic for efficiently ’’recognizing’’ these pattern repetitions at the end of design and postprocessing. Consequently, one can achieve pattern data compaction by large factors, using a hardware step-and-repeat function of the vector scan pattern generator, which is discussed in detail. In order for the step-and-repeat recognition and compaction to be useful, this pattern recognition problem must be solved by a very efficient algorithm, due to the large number of shapes involved. We have developed an algorithm which requires an amount of CPU time which is only linear in the number of shapes in the design, requiring less than one ms/shape on an IBM model 168 computer. We will describe the algorithm in detail and demonstrate its ability to economically compact data sets for LSI by up to an order of magnitude or more.