Efficient Multi-domain Text Recognition Deep Neural Network Parameterization with Residual Adapters
This provides a scalable and adaptable solution for OCR applications in computer vision, though it is incremental in improving efficiency.
The study tackled the problem of deep neural networks requiring extensive data and high computational power for optical character recognition (OCR) across diverse domains, resulting in a model that significantly lowers trainable parameters without sacrificing performance.
Recent advancements in deep neural networks have markedly enhanced the performance of computer vision tasks, yet the specialized nature of these networks often necessitates extensive data and high computational power. Addressing these requirements, this study presents a novel neural network model adept at optical character recognition (OCR) across diverse domains, leveraging the strengths of multi-task learning to improve efficiency and generalization. The model is designed to achieve rapid adaptation to new domains, maintain a compact size conducive to reduced computational resource demand, ensure high accuracy, retain knowledge from previous learning experiences, and allow for domain-specific performance improvements without the need to retrain entirely. Rigorous evaluation on open datasets has validated the model's ability to significantly lower the number of trainable parameters without sacrificing performance, indicating its potential as a scalable and adaptable solution in the field of computer vision, particularly for applications in optical text recognition.