Titlebook: Open-Set Text Recognition; Concepts, Framework, Xu-Cheng Yin,Chun Yang,Chang Liu Book 2024 The Editor(s) (if applicable) and The Author(s),

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Open-Set Text Recognition Implementations(I): Label-to-Representation Mapping, representation space and the label-to-representation mapping module. First, this chapter introduces how characters, or other corresponding granularities, are represented in different methods, i.e., the representation space, where class centers (prototypes) and features extracted from input images r

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Open-Set Text Recognition Implementations(II): Sample-to-Representation Mapping, discussed above (Fig. .). The contents include three parts: feature extraction, representation aggregation, and context handling. Since most mapping approaches break down into a feature extractor and a sampler module, specifically, the feature extractor maps input images to feature maps, whereas th

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Open-Set Text Recognition Implementations(III): Open-set Predictor, in question. For each query instance representation extracted from the sample image, the open-set predictor matches it to representation prototype and determines whether the character belongs to a corresponding class or not. If the instance representation successfully matches a representation proto

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Discussions and Future Directions,ations in OSTR tasks, from different perspectives: recognized language, text recognition granularity, the overall technical route, and open-set classifier design and model implementation. Then we discuss the influence of the Multi-modal Large Language Model on the Open-Set Text Recognition (OSTR) ta

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