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Found 18 papers, 8 papers with code
Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription
State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date, primarily been carried out using monophonic transcription techniques to handle complex score layouts, such as polyphony, often by resorting to simplifications or specific adaptations.
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition
Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document.
Stochastic gradient descent with gradient estimator for categorical features
Categorical data are present in key areas such as health or supply chain, and this data require specific treatment.
Confidence Estimation for Object Detection in Document Images
In the active learning framework, the three first estimators show a significant improvement in performance for the detection of document physical pages and text lines compared to a random selection of images.
Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods
We present a study conducted using three state-of-the-art systems Doc-UFCN, dhSegment and ARU-Net and show that it is possible to build generic models trained on a wide variety of historical document datasets that can correctly segment diverse unseen pages.
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network.
Ranked #1 on
Handwritten Text Recognition
on READ 2016
Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers
However, the segmentation of complex documents into semantic regions is sometimes impossible relying only on visual features and recent models embed both visual and textual information.
SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition
Unconstrained handwriting recognition is an essential task in document analysis.
Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks
In this paper, we introduce a fully convolutional network for the document layout analysis task.
Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition ?
This has a direct influence on the training time of such architectures, with also a direct consequence on the time required to explore various architectures.
Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network
Unconstrained handwritten text recognition is a major step in most document analysis tasks.
End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network
For each text line features, a decoder module recognizes the character sequence associated, leading to the recognition of a whole paragraph.
Ranked #7 on
Handwritten Text Recognition
on IAM
CTCModel: a Keras Model for Connectionist Temporal Classification
We report an extension of a Keras Model, called CTCModel, to perform the Connectionist Temporal Classification (CTC) in a transparent way.
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages.
A syllable based model for handwriting recognition
In this paper, we introduce a new modeling approach of texts for handwriting recognition based on syllables.
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction
Offline handwritten text line recognition is a hard task that requires both an efficient optical character recognizer and language model.
Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon
State-of-the-art methods for handwriting recognition are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN), which now provides very impressive character recognition performance.
Logical segmentation for article extraction in digitized old newspapers
Our back-end system extracts the logical structure of the page to produce the informative units: the articles.
