An empirical study of extracting embedded text from digital images

Abstract

The utilization of images as a means of transferring information is a widespread technique employed to circumvent simple detection functions that primarily focus on analyzing textual content rather than conducting thorough file examinations. This study investigates the efficacy of deep learning models in detecting embedded information within digital images. The data used for analysis was acquired from a secondary source and underwent comprehensive preprocessing. Feature extraction, sequence labeling, and predictive model training were performed using CRNN, CNN, and RNN models. Two specific models were trained and tested in this research: 1) CNN, RNN-LSTM with the Adam optimizer, and 2) CNN, RNN-GRU with the RAdam optimizer for text detection. The findings reveal that Model #1 achieved the highest F1-score during testing, with a score of 98.37% for text detection and 96.73% for word detection. The second model obtained an F1-score of 94.84% and 93.05% for text and word detection, respectively. Model #1 exhibited a word detection accuracy of 98.38% and a text detection accuracy of 96.47%. These findings indicate that the first model outperformed the second model, suggesting that employing RNN-LSTM and the Adam optimizer made a positive impact. Therefore, utilizing deep learning tools and emerging technologies is crucial for extracting textual information and analyzing visual data. In summary, this study concludes that deep learning models can be relied upon to effectively detect textual information embedded within digital images.