In this paper authors have presented a novel approach for Human Machine Interaction (HMI) that can be used to give alphabets as input to internet browser in an interactive Set top box with this added facility. The device is designed with a MEMS sensor which can be worn on fingertip to send alphabetical input to an interactive set top box. Users write the English alphabet in capital letter by just using the sensor attached to their hand. Then this data is undergone some filtering and finally it is sent to the recognition module. (0,1). Final decision about the recognized character is made by using multifactorial approach. We get a recognition accuracy of more than 90%. We have tested the system with the inputs given by 22 users (16 male and 6 female) each of the user is asked to write the each letter 5 times 1.
The features used for recognition consists of chain code based and un-directional un-weighted graph based features. Each of them recognizes the input character with some confidence factor lying in the closed interval
Market demand for an embedded realization of video OCR motivated the authors to exert an attempt to evaluate the performance of existing document image OCR techniques for the same. Thus authors have tried to port the open source OCR systems like GOCR and Tessaract on an embedded platform. But their performance on an embedded platform shows that the character level and word level recognition accuracy is quite unacceptable for video text. This paper compares two such open source OCR systems on Indian TV videos and proposes some techniques that can be used to improve the recognition accuracy from 62% to 93%. Moreover the challenges of porting those codes on an embedded platform is also analyzed in this paper.
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