Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentat...Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded(noise free) UPTI(Urdu Printed Text Image) data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.展开更多
In this paper, we consider the power optimization problem in Orthogonal Frequency Division Multiplexing （OFDM）-based relay-enhanced device-to-device （D2D） communication. In a single cell transmission scenario, dua...In this paper, we consider the power optimization problem in Orthogonal Frequency Division Multiplexing （OFDM）-based relay-enhanced device-to-device （D2D） communication. In a single cell transmission scenario, dual- hop communication is assumed in which each D2D user re-uses the spectrum of just one Cellular User （CU）. In this work, we formulate a joint optimization scheme under a Decode-and-Forward （DF） relaying protocol to maximize the sum throughput of D2D and cellular networks via power allocation over different sub-carriers. The problem is thus transformed into a standard convex optimization, subject to individual power constraints at different transmitting nodes. We exploit the duality theory to decompose the problem into several sub-problems and use Karush-Kuhn- Tucker （KKT） conditions to solve each sub-problem. We provide simulation results to validate the performance of our proposed scheme.展开更多
基金National Natural Science Foundation of China (Project No. 61273365)111 Project (No. B08004) are gratefully acknowledged
文摘Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded(noise free) UPTI(Urdu Printed Text Image) data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.
文摘In this paper, we consider the power optimization problem in Orthogonal Frequency Division Multiplexing （OFDM）-based relay-enhanced device-to-device （D2D） communication. In a single cell transmission scenario, dual- hop communication is assumed in which each D2D user re-uses the spectrum of just one Cellular User （CU）. In this work, we formulate a joint optimization scheme under a Decode-and-Forward （DF） relaying protocol to maximize the sum throughput of D2D and cellular networks via power allocation over different sub-carriers. The problem is thus transformed into a standard convex optimization, subject to individual power constraints at different transmitting nodes. We exploit the duality theory to decompose the problem into several sub-problems and use Karush-Kuhn- Tucker （KKT） conditions to solve each sub-problem. We provide simulation results to validate the performance of our proposed scheme.
