The scanned text image is a non editable image though it has the text but one can not edit it or make any change, if required, to that scanned document. This provides a basis for the optical character recognition (OCR) theory. OCR is the process of recognizing a segmented part of the scanned image as a character. The overall OCR process consists of three major sub processes like pre processing, segmentation and then recognition. Out of these three, the segmentation process is the back bone of the overall OCR process. We can say that the segmentation process is the most significant process because if the segmentation is incorrect then we can not have the correct results; it is just like garbage in and garbage out. But it is not an easy job, because segmentation is one of the complex processes. It is more difficult if the document is handwritten because in that case only few points are there which can be used to make segmentation. In this paper, we formulate an approach to segment the scanned document image. As per this approach, initially this considers the whole image as one large window. Then this large window is broken into less large windows giving lines, once the lines are identified then each window consisting of a line is used to find a word present in that line and finally to characters. For that purpose we used the concept of variable sized window, that is, the window whose size can be adjusted according to needs. This concept was implemented and results were analyzed. After the analysis the same concept was modified and finally tried on different documents and we got good reasonable results.
The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.
Road traffic noise pollution is a global hazard, and rapid urbanization has aggravated the problem. This paper explores a novel approach which involves a smartphone user community to monitor the prevalent noise. The system involves a client application on smartphones that records noise, processes the information and communicates to a server and shares the information as visual noise levels on Google® Maps. A fuzzy logic-based classification of noise is proposed. Results from residential, commercial, and industrial areas of the northern region of India are demonstrated. The noise levels are generally found to be higher than the prescribed standards. The experiment demonstrates the huge potential of user community participation in monitoring noise pollution.
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