The open-set text recognition task is a generalized form of the (close-set) text recognition task, where the model is further challenged to spot and incrementally recognize novel characters not covered by the training data. Novel characters also indicate that the language model of the training set is biased from the "real-world". In this work, we alleviate the confounding effect of such biases by learning from individual character representations isolated from their context. Specifically, we propose a Character-First Open-Set Text Recognition framework that cotrains the feature extractor with two context-free learning tasks. First, a Context Isolation Learning task is proposed to wipe the context for each character from the input image, utilizing a character mask learned in a weak supervision manner. Second, the framework adopts an Individual Character Learning task, which is a single-character classification task with synthetic samples. After training on English and simplified Chinese data, our framework can adapt to recognize unseen characters in Japanese, Korean, Greek, and other scripts without retraining, and can reliably spot unseen characters in Japanese with an F1-score over 64%. The framework also shows 91.5% line accuracy on IIIT5k and a speed of over 69 FPS single-batched, making it a feasible universal lightweight OCR solution that works well for both open-set and close-set use cases.

The open-set text recognition task is an emerging chal-lenge that requires an extra capability to cognize novel characters during evaluation. We argue that a major cause of the limited performance for current methods is the con-founding effect of contextual information over the visual information of individual characters. Under open-set sce-narios, the intractable bias in contextual information can be passed down to visual information, consequently im-pairing the classification performance. In this paper, a Character-Context Decoupling framework is proposed to alleviate this problem by separating contextual information and character-visual information. Contextual information can be decomposed into temporal information and lin-guistic information. Here, temporal information that mod-els character order and word length is isolated with a de-tached temporal attention module. Linguistic information that models n- gram and other linguistic statistics is sepa-rated with a decoupled context anchor mechanism. A va-riety of quantitative and qualitative experiments show that our method achieves promising performance on open-set, zero-shot, and close-set text recognition datasets.

Text detection in complex scene images is a challenging task for intelligent transportation. Recently, anchor mechanisms are widely utilized in scene text detection tasks. However, in existing methods, anchors are generally predefined empirically, degrading robustness to complex scenarios with various sizes and orientation variations. In this paper, we propose a novel Attention Anchor Mechanism (AAM), especially targeting at predicting appropriate anchors for each pixel. To be concrete, we regard a series of predefined anchors as basic anchors and utilize an attention model to predict weights corresponding to basic anchors. Consequently, the weighted sum of basic anchors in each pixel can obtain a predicted anchor. In this way, the gap between the predicted anchors and the corresponding ground truth boxes could be narrowed, making the network easier to regress. For facilitating the design of basic anchors, we adopt a dimension-decomposition mechanism to predict width, height, and angle of anchors, respectively. Extensive experiments on several public datasets demonstrate that our method achieves state-of-the-art performance.

Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection. In our method, an innovative local graph bridges a text proposal model via Convolutional Neural Network (CNN) and a deep relational reasoning network via Graph Convolutional Network (GCN), making our network end-to-end trainable. To be concrete, every text instance will be divided into a series of small rectangular components, and the geometry attributes (e.g., height, width, and orientation) of the small components will be estimated by our text proposal model. Given the geometry attributes, the local graph construction model can roughly establish linkages between different text components. For further reasoning and deducing the likelihood of linkages between the component and its neighbors, we adopt a graph-based network to perform deep relational reasoning on local graphs. Experiments on public available datasets demonstrate the state-of-the-art performance of our method. Code is available at https://github.com/GXYM/DRRG.

Direct regression and anchor are the two mainly effective and prevailing mechanisms in the paradigm of scene text detection. However, the use of direct regression-based methods may be challenging during optimization without the help of anchors as references. Unfortunately, the anchor-based methods always suffer from the careful design of the anchors, degrading the robustness to complex scenes. To address the above-mentioned problems, we propose a novel hidden anchor mechanism (HAM) especially for scene text detection. The predictions of anchors are innovatively regarded as hidden layers, and the weighted sum of the predictions is integrated into a direct regression-based network. Hence, the architecture of our HAM still has the characteristic of simplicity as with direct regression-based methods. Moreover, it is easier to optimize anchors as references with this type of method than with direct regression-based methods. In this way, our network can take advantage of both direct regression and anchor mechanisms. In addition, we decouple three kinds of one-dimensional anchors from three-dimensional anchors, greatly reducing the number of anchors in text bounding box matching without performance degradation. We also propose a post-processing technique for long text detection, named iterative regression box (IRB), which takes a few additional computational costs and can be easily generalized to other methods. Experiments on several public datasets demonstrate that the proposed method achieves state-of-the-art performance. Code is available athttps://github.com/hjbplayer/HAM.