论文标题
场景文本识别的多粒性预测
Multi-Granularity Prediction for Scene Text Recognition
论文作者
论文摘要
多年来，场景文本识别（STR）一直是计算机视觉的积极研究主题。为了解决这个具有挑战性的问题，已经提出了许多创新的方法，并将语言知识纳入STR模型最近已成为一个显着的趋势。在这项工作中，我们首先从视觉变压器（VIT）的最新进展中汲取灵感来构建一种概念上简单而强大的Vision STR模型，该模型建立在VIT和表现以前的场景文本识别的先前最先进的模型，包括纯粹的视觉模型和语言授权方法。为了整合语言知识，我们进一步提出了一种多粒性预测策略，以隐式方式将信息从语言模式注入模型，即NLP中广泛使用的子单词表示（BPE和WordPiece）在输出空间中广泛引入输出空间，除了传统的字符级别表示，而无独立的语言模型（不采用独立语言模型（LM）。所得的算法（称为MGP-STR）能够将Str的性能包络提高到更高的水平。具体而言，它的平均识别精度在标准基准上达到93.35％。代码可从https://github.com/alibabaresearch/advancedliteratemachinery/tree/main/main/ocr/mgp-str获得。
Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet powerful vision STR model, which is built upon ViT and outperforms previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, i.e. , subword representations (BPE and WordPiece) widely-used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push the performance envelop of STR to an even higher level. Specifically, it achieves an average recognition accuracy of 93.35% on standard benchmarks. Code is available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR.
