Advancements and challenges in handwritten text recognition: A comprehensive survey
Handwritten Text Recognition (HTR) is essential for digitizing historical documents in
different kinds of archives. In this study, we introduce a hybrid form archive written in French …
different kinds of archives. In this study, we introduce a hybrid form archive written in French …
Dan: a segmentation-free document attention network for handwritten document recognition
Unconstrained handwritten text recognition is a challenging computer vision task. It is
traditionally handled by a two-step approach, combining line segmentation followed by text …
traditionally handled by a two-step approach, combining line segmentation followed by text …
Gated convolutional recurrent neural networks for multilingual handwriting recognition
In this paper, we propose a new neural network architecture for state-of-the-art handwriting
recognition, alternative to multi-dimensional long short-term memory (MD-LSTM) recurrent …
recognition, alternative to multi-dimensional long short-term memory (MD-LSTM) recurrent …
Segmentation-free handwritten Chinese text recognition with LSTM-RNN
We present initial results on the use of Multi-Dimensional Long-Short Term Memory
Recurrent Neural Networks (MDLSTM-RNN) in recognizing lines of handwritten Chinese …
Recurrent Neural Networks (MDLSTM-RNN) in recognizing lines of handwritten Chinese …
Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering
K Chen, Q Huo - … conference on acoustics, speech and signal …, 2016 - ieeexplore.ieee.org
We present a new approach to scalable training of deep learning machines by incremental
block training with intra-block parallel optimization to leverage data parallelism and …
block training with intra-block parallel optimization to leverage data parallelism and …
Attention-based fully gated CNN-BGRU for Russian handwritten text
This article considers the task of handwritten text recognition using attention-based encoder–
decoder networks trained in the Kazakh and Russian languages. We have developed a …
decoder networks trained in the Kazakh and Russian languages. We have developed a …
Deep neural networks for large vocabulary handwritten text recognition
T Bluche - 2015 - theses.hal.science
The automatic transcription of text in handwritten documents has many applications, from
automatic document processing, to indexing and document understanding. One of the most …
automatic document processing, to indexing and document understanding. One of the most …
ICFHR2014 competition on handwritten text recognition on transcriptorium datasets (HTRtS)
A contest on Handwritten Text Recognition organised in the context of the ICFHR 2014
conference is described. Two tracks with increased freedom on the use of training data were …
conference is described. Two tracks with increased freedom on the use of training data were …
Candidate fusion: Integrating language modelling into a sequence-to-sequence handwritten word recognition architecture
Sequence-to-sequence models have recently become very popular for tackling handwritten
word recognition problems. However, how to effectively integrate an external language …
word recognition problems. However, how to effectively integrate an external language …
Paragraph text segmentation into lines with recurrent neural networks
The detection of text lines, as a first processing step, is critical in all text recognition systems.
State-of-the-art methods to locate lines of text are based on handcrafted heuristics fine-tuned …
State-of-the-art methods to locate lines of text are based on handcrafted heuristics fine-tuned …