HTR
37 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in HTR
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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MNIST
IAM
Most implemented papers
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold
IntuitionMachines/OrigamiNet
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CVPR 2020
On IAM we even surpass single line methods that use accurate localization information during training.
Full Page Handwriting Recognition via Image to Sequence Extraction
kingyiusuen/image-to-latex
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We present a Neural Network based Handwritten Text Recognition (HTR) model architecture that can be trained to recognize full pages of handwritten or printed text without image segmentation.
Boosting Handwriting Text Recognition in Small Databases with Transfer Learning
josarajar/HTRTF
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We first investigate, for a reduced and fixed number of training samples, 350 lines, how the learning from a large database, the IAM, can be transferred to the learning of the CLC of a reduced database, Washington.
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
amzn/convolutional-handwriting-gan
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CVPR 2020
This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.
Data Generation for Post-OCR correction of Cyrillic handwriting
We apply a Handwritten Text Recognition (HTR) model to this dataset to identify OCR errors, forming the basis for our POC model training.
Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
We present the Manuscripts of Handwritten Arabic~(Muharaf) dataset, which is a machine learning dataset consisting of more than 1, 600 historic handwritten page images transcribed by experts in archival Arabic.
Character-Based Handwritten Text Transcription with Attention Networks
When the sequence alignment is one-to-one, softmax attention is able to learn a more precise alignment at each step of the decoding, whereas the alignment generated by sigmoid attention is much less precise.
Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention
This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions.
Handwriting Recognition of Historical Documents with few labeled data
0x454447415244/HandwritingRecognitionSystem
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In this work, we demonstrate how to train an HTR system with few labeled data.
A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition
Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text.
