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. 2024 Jul 22;14(1):16800.
doi: 10.1038/s41598-024-67738-8.

Enhancing handwritten text recognition accuracy with gated mechanisms

Ravikumar Chinthaginjala  1 , C Dhanamjayulu  2 , Tai-Hoon Kim  3 , Suhaib Ahmed  4 , Si-Yeong Kim  5 , A S Kumar  6 , Visalakshi Annepu  7 , Shafiq Ahmad  8
Affiliations

Enhancing handwritten text recognition accuracy with gated mechanisms

Ravikumar Chinthaginjala et al. Sci Rep. .

Abstract

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 HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription.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.

Keywords: Convolutional recurrent neural networks; Deep learning; Gated convolutional neural networks; Handwritten transcript recognition; Natural language processing.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Puigcerver architecture.
Figure 2
Figure 2
Bluche architecture.
Figure 3
Figure 3
Flor architecture.
Figure 4
Figure 4
Proposed architecture.
Figure 5
Figure 5
Bentham database sample.
Figure 6
Figure 6
IAM database sample.
Figure 7
Figure 7
RIMES database sample.
Figure 8
Figure 8
Saint Gall database sample.
Figure 9
Figure 9
Washington database sample.
Figure 10
Figure 10
WER and CER evaluation for all Test partition.
See this image and copyright information in PMC

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