DeepSolo++：让带有显式点的Transformer解码器独当多语言文本识别
DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting
AI总结：
本文提出DeepSolo++，一种基于Transformer解码器和显式点查询的端到端多语言文本识别方法，同时处理检测、识别和脚本识别，性能优于现有方法且扩展性强。
摘要：
端到端文本识别旨在将场景文本检测和识别整合到一个统一的框架中。处理这两个子任务之间的关系在设计有效的识别器中起着关键作用。尽管基于Transformer的方法消除了启发式后处理，但它们仍然面临子任务之间的协同问题和训练效率低下的问题。此外，它们忽视了多语言文本识别的探索，这需要额外的脚本识别任务。在本文中，我们提出了DeepSolo++，一个简单的类似DETR的基线，让一个带有显式点的解码器同时独当文本检测、识别和脚本识别。技术上，对于每个文本实例，我们将字符序列表示为有序点，并用可学习的显式点查询进行建模。经过一个解码器后，点查询已经编码了必要的文本语义和位置，因此可以进一步通过非常简单的预测头并行解码为文本的中心线、边界、脚本和置信度。此外，我们展示了我们的方法在字符类别、语言类型和任务方面的惊人扩展性。一方面，我们的方法不仅在英文场景中表现良好，还掌握了具有复杂字体结构和千级字符类别的转录，例如中文。另一方面，我们的DeepSolo++在额外引入的脚本识别任务上取得了更好的性能，并且训练流程比之前的方法更简单。此外，我们的模型也兼容线标注，这比多边形标注的成本要低得多。代码可在\url{https://github.com/ViTAE-Transformer/DeepSolo}获取。
英文摘要：
End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. Besides, they overlook the exploring on multilingual text spotting which requires an extra script identification task. In this paper, we present DeepSolo++, a simple DETR-like baseline that lets a single decoder with explicit points solo for text detection, recognition, and script identification simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, our method not only performs well in English scenes but also masters the transcription with complex font structure and a thousand-level character classes, such as Chinese. On the other hand, our DeepSolo++ achieves better performance on the additionally introduced script identification task with a simpler training pipeline compared with previous methods. In addition, our models are also compatible with line annotations, which require much less annotation cost than polygons. The code is available at \url{https://github.com/ViTAE-Transformer/DeepSolo}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
