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Found 13 papers, 6 papers with code
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.
Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild
This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images.
Graph-based Deep Generative Modelling for Document Layout Generation
One of the major prerequisites for any deep learning approach is the availability of large-scale training data.
DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis
The results highlight that our model can successfully generate realistic and diverse document images with multiple objects.
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach
This paper presents a novel method for date estimation of historical photographs from archival sources.
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting
In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder.
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.
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.
Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval
Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic.
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.
