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The objective of the paper is to recognize handwritten samples of Roman numerals using Tesseract open source Optical Character Recognition (OCR) engine. Tesseract is trained with data samples of different persons to generate one user-independent language model, representing the handwritten Roman digit-set. The system is trained with 1226 digit samples(More)
With the advancement of ubiquitous computing, new types of Wireless sensor networks (WSNs) have emerged where sensors perform their tasks even as their surrounding network neighborhood changes, nodes terminate unexpectedly and signal strengths vary dynamically. In such scenarios, it is very important to use efficient service discovery algorithms adapt(More)
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