In recent years, blended learning has grown to occupy an important space in Chinese educational practice. Policymakers have developed many application strategies and platforms and are continuing to develop BL for future use. In order to apply BL in practice, key stakeholders have been using different learning management systems, digital platforms, games, hybrid courses, and various forms of social media to create a framework for BL. This study asserts that many visible opportunities have emerged in Chinese higher education through the (...) applications of BL. The advantages of BL are that it fosters stronger academic achievement, student engagement, and cognitive engagement and understanding as well as flexible and quick communication skills, faster interaction skills, technical skills, and adaptability to ever-changing educational practices. On the other hand, BL has brought about some pedagogical and technical difficulties for both learners and practitioners. This study found that most BL courses are not as effective as they could be because they do not have a strong pedagogical framework. Moreover, BL suffers from the technical incompetence of teachers and students, the inefficiency of LMSs, and the unavailability of required resources, such as certain devices and the Internet. Some higher education institutions have become pioneers in Chinese educational practice and been able to successfully adopt BL frameworks and integrate Moodle as well as other platforms and techniques. However, many other institutions’ attempts to adopt BL approaches have not been as effective. In order to better understand how and in what ways BL is being integrated into the educational system, this study overviews the current situation and discusses the strengths and weaknesses of BL in Chinese higher education. (shrink)

. Effective text region extraction and binarization of image embedded text documents on mobile devices having limited computational resources is an open research problem. In this paper, we present one such technique for preprocessing images captured with built-in cameras of handheld devices with an aim of developing an efficient Business Card Reader. At first, the card image is processed for isolating foreground components. These foreground components are classified as either text or non-text using different feature descriptors of texts and images. (...) The non-text components are removed and the textual ones are binarized with a fast adaptive algorithm. Specifically, we propose new techniques for foreground component isolation, text extraction and binarization of text regions from camera captured business card images. Experiments with business card images of various resolutions show that the present technique yields better accuracy and involves low computational overhead in comparison with the state-of-the-art. We achieve optimum text/non-text separation performance with images of resolution 800×600 pixels with an average recall rate of 93.90% and a precision rate of 96.84%. It involves a peak memory consumption of 0.68 MB and processing time of 0.102 seconds on a moderately powerful notebook, and 4 seconds of processing time on a PDA. (shrink)