Handwriting Recognition
50 papers with code • 3 benchmarks • 20 datasets
Benchmarks
These leaderboards are used to track progress in Handwriting Recognition
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Libraries
Use these libraries to find Handwriting Recognition models and implementations
IAM
DeepWriting
RIMES
HKR
Bentham
KOHTD
Schiller
Ricordi
Most implemented papers
Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
IntuitionMachines/OrigamiNet
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Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges.
No Padding Please: Efficient Neural Handwriting Recognition
Neural handwriting recognition (NHR) is the recognition of handwritten text with deep learning models, such as multi-dimensional long short-term memory (MDLSTM) recurrent neural networks.
Field typing for improved recognition on heterogeneous handwritten forms
Offline handwriting recognition has undergone continuous progress over the past decades.
GEVO: GPU Code Optimization using Evolutionary Computation
If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 51. 08%.
HKR For Handwritten Kazakh & Russian Database
The database consists of more than 1400 filled forms.
AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning
I propose a state of the art deep neural architectural solution for handwritten character recognition for Bengali alphabets, compound characters as well as numerical digits that achieves state-of-the-art accuracy 96. 8% in just 11 epochs.
Differentiable Weighted Finite-State Transducers
We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time.
Motion-Based Handwriting Recognition and Word Reconstruction
deep-scribe/handwriting-recognition
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In this project, we leverage a trained single-letter classifier to predict the written word from a continuously written word sequence, by designing a word reconstruction pipeline consisting of a dynamic-programming algorithm and an auto-correction model.
Motion-Based Handwriting Recognition
RussellXie7/motion-based-handwriting-recognition
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We attempt to overcome the restriction of requiring a writing surface for handwriting recognition.
Fine-tuning Handwriting Recognition systems with Temporal Dropout
0x454447415244/HandwritingRecognitionSystem
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This paper introduces a novel method to fine-tune handwriting recognition systems based on Recurrent Neural Networks (RNN).
