A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors

Malware Detection Using Dynamic Analysis

In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach