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.

A Novel Universal Steganalysis Algorithm Based on the IQM and the SRM

The state-of-the-art universal steganalysis method,spatial rich model(SRM),and the steganalysis method using image quality metrics(IQM)are both based on image residuals,while they use 34671 and 10 features respectively.This paper proposes a novel steganalysis scheme that combines their advantages in two ways.First,filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods.In addition,a total variant(TV)filter is also used due to its good performance of preserving image edge properties during filtering.Second,due to each type of these filters having own advantages,the multiple filters are used simultaneously and the features extracted from their outputs are combined together.The whole steganalysis procedure is removing steganographic noise using those filters,then measuring the distances between images and their filtered version with the image quality metrics,and last feeding these metrics as features to build a steganalyzer using either an ensemble classifier or a support vector machine.The scheme can work in two modes,the single filter mode using 9 features,and the multi-filter mode using 639 features.We compared the performance of the proposed method,the SRM and the maxSRMd2.The maxSRMd2 is the improved version of the SRM.The simulated results show that the proposed method that worked in the multi-filter mode was about 10%more accurate than the SRM and maxSRMd2 when the data were globally normalized,and had similar performance with the SRM and maxSRMd2 when the data were locally normalized.