Restoring texts corrupted by visually perturbed homoglyph characters presents significant challenges to conventional Natural Language Processing (NLP) systems, primarily due to ambiguities arising from characters that appear visually similar yet differ semantically. Traditional text restoration methods struggle with these homoglyph perturbations due to limitations such as a lack of contextual understanding and difficulty in handling cases where one character maps to multiple candidates. To address these issues, we propose an Optical Character Recognition (OCR)-assisted masked Bidirectional Encoder Representations from Transformers (BERT) model specifically designed for homoglyph-perturbed text restoration. Our method integrates OCR preprocessing with a character-level BERT architecture, where OCR preprocessing transforms visually perturbed characters into their approximate alphabetic equivalents, significantly reducing multi-correspondence ambiguities. Subsequently, the character-level BERT leverages bidirectional contextual information to accurately resolve remaining ambiguities by predicting intended characters based on surrounding semantic cues. Extensive experiments conducted on realistic phishing email datasets demonstrate that the proposed method significantly outperforms existing restoration techniques, including OCR-based, dictionary-based, and traditional BERT-based approaches, achieving a word-level restoration accuracy of up to 99.59% in fine-tuned settings. Additionally, our approach exhibits robust performance in zero-shot scenarios and maintains effectiveness under low-resource conditions. Further evaluations across multiple downstream tasks, such as part-of-speech tagging, chunking, toxic comment classification, and homoglyph detection under conditions of severe visual perturbation (up to 40%), confirm the method’s generalizability and applicability. Our proposed hybrid approach, combining OCR preprocessing with character-level contextual modeling, represents a scalable and practical solution for mitigating visually adversarial text attacks, thereby enhancing the security and reliability of NLP systems in real-world applications.

Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems. The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference, such as background noise, overlapping speech, and visual obstructions. The proposed framework employs a structured fusion approach, combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms. Audio data are transformed into mel-spectrogram representations, and visual data are represented as raw frame sequences. Spatial and temporal features are extracted through convolutional and transformer-based encoders, allowing the framework to capture complementary and hierarchical information from both sources. A cross-modal attention module enables selective emphasis on relevant signals while suppressing modality-specific noise. Performance is validated on a modified version of the AFEW dataset, in which controlled noise is introduced to emulate realistic conditions. The framework achieves higher classification accuracy than comparative baselines, confirming increased robustness under conditions of cross-modal disruption. This result demonstrates the suitability of the proposed method for deployment in practical emotion-aware technologies operating outside controlled environments. The study also contributes a systematic approach to fusion design and supports further exploration in the direction of resilient multimodal emotion analysis frameworks. The source code is publicly available at https://github.com/asmoon002/AVER (accessed on 18 August 2025).