HTR
28 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in HTR
| Trend | Dataset | Best Model | Paper | Code | Compare |
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IAM
Most implemented papers
SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators
MattAlexMiracle/SmartPatch
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As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation.
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement
Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable.
StackMix and Blot Augmentations for Handwritten Text Recognition
This paper proposes a handwritten text recognition(HTR) system that outperforms current state-of-the-artmethods.
KOHTD: Kazakh Offline Handwritten Text Dataset
In this regard, there is a need to implement Handwritten Text Recognition (HTR) which is an automatic way to decrypt records using a computer.
Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes
planet-ai-gmbh/tfaip-hybrid-ctc-s2s
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The CTC confidences are computed on the encoder while the Transformer is only used for character-wise S2S decoding.
Continuous Offline Handwriting Recognition using Deep Learning Models
For the design of this new model, an extensive analysis of the capabilities of different convolutional architectures in the simplified problem of isolated character recognition has been carried out in order to identify the most suitable ones to be integrated into the continuous model.
Evaluation of HTR models without Ground Truth Material
The evaluation of Handwritten Text Recognition (HTR) models during their development is straightforward: because HTR is a supervised problem, the usual data split into training, validation, and test data sets allows the evaluation of models in terms of accuracy or error rates.
AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems.
Transformer-based HTR for Historical Documents
EriCongMa/awesome-transformer-ocr
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We apply the TrOCR framework to real-world, historical manuscripts and show that TrOCR per se is a strong model, ideal for transfer learning.
BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation
Our line segmentation approach takes care of the variability involved in different writing styles, accurately segmenting complex handwritten text lines of curvilinear nature.
