Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Latency-Aware Dynamic Second Offloading Service in SDN-Based Fog Architecture

Task offloading is a key strategy in Fog Computing (FC). Thedefinition of resource-constrained devices no longer applies to sensors andInternet of Things (IoT) embedded system devices alone. Smart and mobileunits can also be viewed as resource-constrained devices if the power, cloudapplications, and data cloud are included in the set of required resources. Ina cloud-fog-based architecture, a task instance running on an end device mayneed to be offloaded to a fog node to complete its execution. However, ina busy network, a second offloading decision is required when the fog nodebecomes overloaded. The possibility of offloading a task, for the second time,to a fog or a cloud node depends to a great extent on task importance, latencyconstraints, and required resources. This paper presents a dynamic service thatdetermines which tasks can endure a second offloading. The task type, latencyconstraints, and amount of required resources are used to select the offloadingdestination node. This study proposes three heuristic offloading algorithms.Each algorithm targets a specific task type. An overloaded fog node can onlyissue one offloading request to execute one of these algorithms accordingto the task offloading priority. Offloading requests are sent to a SoftwareDefined Networking (SDN) controller. The fog node and controller determinethe number of offloaded tasks. Simulation results show that the average timerequired to select offloading nodes was improved by 33% when compared tothe dynamic fog-to-fog offloading algorithm. The distribution of workloadconverges to a uniform distribution when offloading latency-sensitive nonurgenttasks. The lowest offloading priority is assigned to latency-sensitivetasks with hard deadlines. At least 70% of these tasks are offloaded to fognodes that are one to three hops away from the overloaded node.