Efficient Multi-domain Text Recognition Deep Neural Network
  Parameterization with Residual Adapters
Jiayou Chao Wei Zhu
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.