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Recent advancements in deep neural networks have markedly enhanced theperformance of computer vision tasks, yet the specialized nature of thesenetworks often necessitates extensive data and high computational power.Addressing these requirements, this study presents a novel neural network modeladept at optical character recognition (OCR) across diverse domains, leveragingthe strengths of multi-task learning to improve efficiency and generalization.The model is designed to achieve rapid adaptation to new domains, maintain acompact size conducive to reduced computational resource demand, ensure highaccuracy, retain knowledge from previous learning experiences, and allow fordomain-specific performance improvements without the need to retrain entirely.Rigorous evaluation on open datasets has validated the model's ability tosignificantly lower the number of trainable parameters without sacrificingperformance, indicating its potential as a scalable and adaptable solution inthe field of computer vision, particularly for applications in optical textrecognition.

Efficient Multi-domain Text Recognition Deep Neural Network Parameterization with Residual Adapters