Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition

Li, Bingcong; Tang, Xin; Qi, Xianbiao; Chen, Yi-Hao; Xiao, Rong

doi:10.48550/arxiv.2009.10874

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Taking into account the transformer-based methods have the advantages of parallel computation in the field of natural language processing, Zhu et al [15] directly combined heavy backbone and the whole transformer network as a new text recognizer, and a hierarchical attention mechanism including four self-attention blocks was proposed in the encoding part to describe context information, which is also a heavy model. To make text recognition technology more suitable for real-world applications, Li et al [26] proposed a transformer-based model, a self-attention mechanism was applied to text recognition to reach a better ideal state. This model utilized localitysensitive hashing instead of softmax regression to compress model.…”

Section: Related Workmentioning

confidence: 99%

Scene text recognition via context modeling for low-quality image in logistics industry

Heng

Guan

et al. 2022

Complex Intell. Syst.

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Text recognition has been applied in many fields recently, such as robot vision, video retrieval, and scene understanding. However, minimal research has been conducted in the field of logistics wherein images of express sheets captured by cameras are mostly curved, distorted, and have low resolution. In this study, a new method is proposed to address the aforementioned research gap while simultaneously considering irregular and low-resolution English letters. The entire approach comprises a rectification module, a convolutional neural network (CNN) extractor, a semantic context module (SCM), a global context module (GCM), and a lightweight transformer decoder that can exhibit improved training speed. In particular, we propose the idea of context modeling in our proposed method. (1) The proposed SCM is introduced to capture full-image dependencies and generates rich semantic context information. (2) We propose the GCM, which not only enhances long-range dependencies from the output of SCM but also outputs abundant pixel information to the self-attention decoder. (3) To solve the low-resolution text recognition problem in a large number of express sheet scenes, we propose Chinese datasets for improving intelligent logistics. Experiments conducted on six public benchmarks demonstrate that the developed method achieves better robustness to low-resolution and irregular text images.

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Section: Related Workmentioning

confidence: 99%

Scene text recognition via context modeling for low-quality image in logistics industry

Heng

Guan

et al. 2022

Complex Intell. Syst.

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“…Moreover, Hamming optical character recognition (OCR) was proposed on the basis of the transformer structure, which solves the problem of too many categories leading to too many model parameters. (11) In recent years, most of the research on text recognition has been carried out simultaneously with text detection, and end-to-end detection recognition models such as fast oriented text spotting (FOTS) and (Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text) PAN++ have been proposed, both of which have achieved good results. (12,13) Named entity recognition (NER) methods aim at recognizing entities of interest in text such as location, organization, and time.…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Text-based Traffic Signs

Chi¹,

Luo²,

Liang³

et al. 2023

Sensors and Materials

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The detection and recognition of text-based traffic signs are important in the field of automatic driving, but these tasks pose problems in practical applications, such as low accuracy in text detection and extraction, poor long-text extraction, and a lack of datasets. To solve these problems and to improve the detection and recognition accuracy of text-based traffic signs so that they can better serve automated driving, we propose an improved Advanced efficiency and accuracy scene test (EAST) model and fixed-size prediction to enhance the capability of extracting features. The text recognition stage features a text preprocessing method that trains convolutional recurrent neural network (CRNN) models using synthetic datasets of Chinese strings. Experimental results show that the improved Advanced EAST model and fixed-size prediction enabled the detection of text on traffic signs to achieve a 96% recall rate and an 88.5% accuracy rate; we also saw better results in the case of dense text and obscuration. Thus, in the absence of targeted datasets, the designed text image preprocessing method can realize print text recognition in different scenarios only by training models using synthetic data, thereby eliminating the need for a large amount of work on training dataset labeling while still meeting requirements of detection and recognition.

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Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition

Cited by 2 publications

References 29 publications

Scene text recognition via context modeling for low-quality image in logistics industry

Scene text recognition via context modeling for low-quality image in logistics industry

Detection and Identification of Text-based Traffic Signs

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