Search Results for author:
Found 8 papers, 2 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.
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.
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.
On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models
The derivational entropy in a finite-state automaton is computed from the probability that is accumulated in all of its individual state sequences.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
