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HTR-Flor++

Proceedings of the ACM Symposium on Document Engineering 2020, 2020
Offline Handwritten Text Recognition (HTR) is a task that offers a challenge in computer vision, where images are the only source of information. In fact, several approaches to optical models have been developed, such as through of Hidden Markov Model (HMM) or recurrent Bidirectional/Multidimensional layers.
Arthur Flor de Sousa Neto   +3 more
openaire   +1 more source

Spike-HTR: Spiking Neural Transformer for Handwritten Text Recognition

Handwritten Text Recognition (HTR) is computationally imbalanced in two ways: most image pixels are background, and many width-axis sequence positions are blank-dominated. This creates a mismatch for Spiking Neural Networks (SNNs): handwriting is observed as a static image, whereas spiking computation unfolds over timesteps.
Liang, Xiubo   +5 more
openaire   +1 more source

HTR-HSS: Hybrid State-Space Modeling for Offline Handwritten Text Recognition

To address the quadratic computational cost of self-attention in Offline Handwritten Text Recognition (HTR), we propose HTR-HSS, a parameter-efficient hybrid architecture for efficient long-sequence modeling. The proposed model combines a lightweight CSP-UNet backbone for multi-scale visual feature extraction, a temporal downsampling module for ...
Yuan Pan   +3 more
openaire   +1 more source

Handwritten Text Recognition (HTR) and manuscript studies

In the lecture fundamentals of manuscript studies in Digital Humanities framework were addressed, followed by in-deep demonstration of logic and models of HTR.
openaire  

End-to-End page-Level assessment of handwritten text recognition

Pattern Recognition, 2023
Alejandro Héctor Toselli   +2 more
exaly  

Deep Neural Network Optimization for Handwritten Text Recognition

Advances in Intelligent Systems and Computing, 2021
S Indu
exaly  

Transforming scholarship in the archives through handwritten text recognition: Transkribus as a case study

Journal of Documentation, 2019
Alejandro Héctor Toselli   +2 more
exaly  

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