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Found 22 papers, 8 papers with code
The Common Optical Music Recognition Evaluation Framework
The quality of Optical Music Recognition (OMR) systems is a rather difficult magnitude to measure.
I Can't Believe It's Not Better: In-air Movement For Alzheimer Handwriting Synthetic Generation
Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models.
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition
In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition.
Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis
Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients.
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches
Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods.
The RPM3D project: 3D Kinematics for Remote Patient Monitoring
This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches.
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts.
Content and Style Aware Generation of Text-line Images for Handwriting Recognition
Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content.
Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement
In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement.
DocEnTr: An End-to-End Document Image Enhancement Transformer
Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties.
Ranked #1 on
Binarization
on H-DIBCO 2011
Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition
Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the non-annotated data.
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.
Ranked #1 on
Binarization
on H-DIBCO 2016
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models).
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.
Learning Graph Edit Distance by Graph Neural Networks
The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies.
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition
Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words.
Ranked #8 on
Handwritten Text Recognition
on IAM
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content.
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture
The main challenge faced when training a language model is to deal with the language model corpus which is usually different to the one used for training the handwritten word recognition system.
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages
In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks.
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data.
Hierarchical stochastic graphlet embedding for graph-based pattern recognition
Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques.
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks.
