End-to-end attention convolutional recurrent network for online handwritten Chinese text recognition

Qu, Xiwen; Wu, Zhihong; Huang, Jun

doi:10.1007/s11042-023-17987-y

Multimed Tools Appl

2024

DOI: 10.1007/s11042-023-17987-y

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End-to-end attention convolutional recurrent network for online handwritten Chinese text recognition

Xiwen Qu,

Zhihong Wu,

Jun Huang

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Enhancing handwritten text recognition accuracy with gated mechanisms

Chinthaginjala,

Dhanamjayulu,

Kim

et al. 2024

Sci Rep

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Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages. The gated mechanism enables the model to capture long-term dependencies, retain relevant context, handle variable length sequences, mitigate error propagation, and adapt to contextual variations. The pipeline involves preprocessing the handwritten text images, extracting features, modeling the sequential dependencies using the gated mechanism, and decoding the output into readable text. The training process utilizes annotated datasets and optimization techniques to minimize transcription discrepancies. HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription. The results show improved accuracy and robustness compared to traditional HTR approaches. The advancements in HTR using a gated mechanism open up new possibilities for effectively recognizing and transcribing handwritten text in various domains. This research does a better job than the most recent iteration of the HTR system when compared to five different handwritten datasets (Washington, Saint Gall, RIMES, Bentham and IAM). Smartphones and robots are examples of low-cost computing devices that can benefit from this research.

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Enhancing handwritten text recognition accuracy with gated mechanisms

Chinthaginjala,

Dhanamjayulu,

Kim

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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End-to-end attention convolutional recurrent network for online handwritten Chinese text recognition

Cited by 1 publication

References 35 publications

Enhancing handwritten text recognition accuracy with gated mechanisms

Enhancing handwritten text recognition accuracy with gated mechanisms

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