Hand written digit recognition is highly nonlinear problem. Recognition of handwritten numerals plays an active role in day to day life now days. Office automation, e-governors and many other areas, reading printed or handwritten documents and convert them to digital media is very crucial and time consuming task. So the system should be designed in such a way that it should be capable of reading handwritten numerals and provide appropriate response as humans do. However, handwritten digits are varying from person to person because each one has their own style of writing, means the same digit or character/word written by different writer will be different even in different languages. This paper presents survey on handwritten digit recognition systems with recent techniques, with three well known classifiers namely MLP, SVM and k-NN used for classification. This paper presents comparative analysis that describes recent methods and helps to find future scope.
General TermsFeature Extraction methods for digit recognition.
There are various approaches for the noise detection in the audio files. Human ear can detect the sound intensity change of 34 milliseconds. In this paper propose novel method for noisy node detection in the wave file. The wave file is divided in chunks depending on Vivaldi concept. Iterative k-means clusters are created. The silhouette noisy node is detected, by comparing silhouette nodes in all iteration for each chunk.
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