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The task of scene text recognition involves processing information from two modalities: images and text, thereby requiring models to have the ability to extract features from images and model sequences simultaneously. Although linguistic knowledge greatly aids scene text recognition tasks, the extensive use of language models in sequence modeling and model prediction stages in recent years has made model architectures increasingly complex and inefficient. In this paper, we propose LCSTR, a pure convolutional visual model that can complete text recognition without the need for attention mechanisms or language models. This approach applies large kernels to text recognition tasks for the first time, extracting word-level text information through large text-aware blocks, capturing long-range dependencies between characters, and using small text-aware blocks to obtain local features within characters. Experiments show that this model strikes a good trade-off between accuracy and speed, achieving notable results on seven public benchmarks, validating the generalizability and effectiveness of this method. Furthermore, owing to the absence of a language module, this model demonstrates remarkable accuracy even in limited sample scenarios, and the lightweight and low computational overhead features make it suitable for engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents a new approach called the empirical mode decomposition - window fractal (EMDWF) algorithm in classification of fingerprint of medicinal herbs. In this way, we consider a glycyrrhiza fingerprint of medicinal herb as a signal sequence, and apply empirical mode decomposition (EMD) and Hiaguchis fractal dimension to construct a feature vector. By using EMD, the glycyrrhiza fingerprint of medicinal herb can be decomposed into some intrinsic mode functions (IMFs). As window fractal dimension (WFD) is applied to each IMF and original signal, the features of the glycyrrhiza fingerprint of medicinal herb can be obtained. Thereafter, SVM is applied as a classifier. The results of the experiments state clearly that the feature extracted by EMDWF is better than that of the existing methods including the pure EMD. With the increase of the number of training samples and the increase of the number of layers in EMD, the classification result achieves more stability. [ABSTRACT FROM AUTHOR]

Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and these training sets of two documents are compared. Because the keywords are similar to signature, the handwriting identification problem is transformed into signature verification problem. Experiments on HIT-MW, HIT-SW and CASIA show this method outperforms many text-independent handwriting identification methods. [ABSTRACT FROM AUTHOR]