A Deep Learning Framework for Extracting and Summarizing Text from Images

Abbas EL DOR · Osama Emad Abdulhussein

In the digital era, substantial amounts of textual information are embedded in images, especially across news outlets, social platforms, and scanned documents. This presents a significant technical challenge: efficiently extracting and summarizing text from images in an automated way that preserves context and meaning. Traditional text summarization techniques are not directly applicable to image-based content because they depend on pre-structured input text. In this paper, we propose a framework that integrates Optical Character Recognition (OCR) and advanced Natural Language Processing (NLP) models to address this challenge. The proposed method implements OCR to extract raw text from images, followed by deep learning-based summarization using models such as LSTM, Bi-LSTM, BERT and T5. These models are trained on large-scale news datasets to enhance their ability to generate coherent summaries from unstructured text. To ensure accessibility and practical usability, our framework is deployed via an interactive web-based interface that allows end-users to upload images and receive concise summaries in real time. Experimental evaluation demonstrates the efficacy of the proposed approach, particularly with transformer-based models, in delivering high-quality summarization from visual text sources

Enhancing Malware Detection Through Machine Learning Techniques

Zeina S. Jassim · Mohamad M. Kassir

Malware detection is important to computer network security since it is the principal attack vector against modern enterprises. As a result, firms must remove viruses from computer systems. Using artificial intelligence, namely machine learning techniques, to function in real-time with an IT system is the ideal solution to this problem. This issue has yet to be fixed, but it is still significant because a lack of processing power and memory constrains these features. The most popular method for evaluating systems and intrusion detection models is using the Application Program Interface (API) calls via the KDD-CUP99 data set to give this solution. KDD-CUP99 has more than three hundred thousand samples, each with 54 features. However, the data set attributes were designed and chosen to provide us with a high malware detection rate. The quality of this data was lowered to produce results. To get the desired results, the attributes of this data were reduced. Data transformation and purification are used in this process. Inaccurate, unnecessary, duplicated, or missing information is eliminated by data cleansing. Data cleaning eliminates inaccurate, excessive, redundant, or lacking information. By comparing this study to earlier research that employed lengthy sequences of software interface (API) calls with the same machine-learning classifiers, data transformation includes discretization, which transforms the continuous process of discretizing continuous data into discrete forms is a type of data transformation. Using more advanced algorithms to do the task at hand with the best precision and the least expense increases accuracy and performance. The data set was divided into two categories using a Support Vector Machine (SVM), Decision Tree (DT), and Iterative Dichotomiser 3 (ID3). The findings revealed that little previous research uses a five-class classification strategy for malware detection. The accuracy of several works is comparable to the accuracy acquired in the proposed work.