语境感知并行解码器用于场景文本识别
概括
本研究介绍了用于场景文本识别 (STR) 的上下文感知并行解码器 (CPPD). CPPD实现了高精度和快速推断速度,在英语和中文文本识别任务中表现优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 场景文本识别 (STR) 方法面临着平衡准确性和推断速度的挑战.
- 自动回归 (AR) 模型提供高精度但处理速度缓慢.
- 平行解码 (PD) 模型是快速的,但不那么准确.
研究的目的:
- 开发一种新的STR方法,实现AR级准确度和PD级速度.
- 引入语境感知并行解码器 (CPPD) 进行增强的STR.
主要方法:
- CPPD集成了字符计数和排序模块,用于上下文感知.
- 这些模块可以预测字符的出现,阅读顺序和位置.
- 一个注意力机制利用这种上下文来准确预测角色.
主要成果:
- CPPD模型表现出具有竞争力的准确性,与领先模型相比,推断速度明显更快.
- 将CPPD模块集成到现有的STR解码器中,提高了它们的准确性.
- 对英语和中文文本识别基准进行了实验.
结论:
- 拟议的CPPD有效地解决了STR.中的准确性-速度权衡问题.
- CPPD为高效准确的场景文本识别提供了一个有前途的方法.
- 模块化设计允许轻松集成和改进现有的STR系统.
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