A Novel Connectionist System for Unconstrained Handwriting Recognition

@article{Graves2009ANC,
  title={A Novel Connectionist System for Unconstrained Handwriting Recognition},
  author={Alex Graves and Marcus Liwicki and Santiago Fern{\'a}ndez and Roman Bertolami and Horst Bunke and J{\"u}rgen Schmidhuber},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2009},
  volume={31},
  pages={855-868}
}
Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on… Expand
Global Context for improving recognition of Online Handwritten Mathematical Expressions
TLDR
This paper presents a temporal classification method for all three subtasks of symbol segmentation, symbol recognition and relation classification in online handwritten mathematical expressions (HMEs) and shows the effectiveness of the proposed method on the two latest CROHME datasets. Expand
A Transformer-Based Fusion Recommendation Model For IPTV Applications
TLDR
This paper presents a deep model for IPTV applications, which generate recommendations using the implicit feedback of users, and a fusion layer is designed on top of the Transformer framework to obtain the semantic preferences of audiences based on their behavior sequences. Expand
DeepCIN: Attention-Based Cervical histology Image Classification with Sequential Feature Modeling for Pathologist-Level Accuracy
TLDR
A network pipeline, DeepCIN, to analyze high-resolution epithelium images hierarchically by focusing on localized vertical regions and fusing this local information for determining Normal/CIN classification achieves pathologist-level CIN classification accuracy. Expand
Vehicle License Plate Recognition In Complex Scenes
  • Zhuang Liu, Yuanping Zhu
  • Computer Science
  • 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)
  • 2020
TLDR
The experimental results show that compared with the existing license plate recognition algorithm, the algorithm in this paper improves significantly the accuracy, and it averages 7.7% in complex scenarios of CCPD dataset. Expand
Offline Handwriting Recognition on Devanagari Using a New Benchmark Dataset
TLDR
This paper releases a new handwritten word dataset for Devanagari, IIIT-HW-Dev, and empirically shows that usage of synthetic data and cross lingual transfer learning helps alleviate the issue of lack of training data. Expand
Towards Accurate Handwritten Word Recognition for Hindi and Bangla
TLDR
This work demonstrates an end-to-end trainable CNN-RNN hybrid architecture which takes inspirations from recent advances of using residual blocks for training convolutional layers, along with the inclusion of spatial transformer layer to learn a model invariant to geometric distortions present in handwriting. Expand
Connectionist Temporal Classification for Offline Handwritten Text Recognition
Handwritten text recognition is an important problem that has many applications such as automatic indexing and transcription of historical documents. In the last decade, there have been significantExpand
Context-enhanced text recognition using semantic vector space models
Contextual information is important to correctly recognize text. Several methods exist to use contextual information in text recognition systems, most notably statistical language models. However,Expand
Détection de mots clés et d'expressions régulières en vue de la reconnaissance d'entités nommées dans des documents manuscrits. (Keyword detection and regular expression spotting for named entity recognition in handwritten documents)
TLDR
La deuxieme contribution est un systeme generique de detection de mots cles et d’expressions regulieres permettant of detecter n’importe quelle sequence dans une ligne of texte. Expand
RECOGNITION OF HANDWRITTEN DEVANAGARI WORDS USING NEURAL NETWORK
Handwritten Word Recognition is an important problem of Pattern Recognition. Online handwritten recognition system for Devanagari words is still in developing stage and becoming challenging due toExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 66 REFERENCES
A Survey On Off-Line Cursive Script Recognition
Recognition of cursive Roman handwriting: past, present and future
  • H. Bunke
  • Computer Science
  • Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.
  • 2003
TLDR
The state of the art in off-line Roman cursive handwriting recognition is reviewed, recent trends are analyzed, and challenges for future research in this field are identified. Expand
HMM-Based Online Handwriting Recognition System for Telugu Symbols
TLDR
An online handwritten symbol recognition system for Telugu, a widely spoken language in India, is presented based on hidden Markov models (HMM) and uses a combination of time-domain and frequency-domain features. Expand
Handwriting Recognition: Tablet PC Text Input
TLDR
High-end versions of Microsoft's Vista now include tablet PC software, with an improved recognizer that supports both personalization and error reporting, and a time-delay neural network working with a lexicon. Expand
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks
TLDR
A system capable of directly transcribing raw online handwriting data is described, consisting of an advanced recurrent neural network with an output layer designed for sequence labelling, combined with a probabilistic language model. Expand
Unconstrained Online Handwriting Recognition with Recurrent Neural Networks
In online handwriting recognition the trajectory of the pen is recorded during writing. Although the trajectory provides a compact and complete representation of the written output, it is hard toExpand
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
TLDR
This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing. Expand
HMM-Based On-Line Recognition of Handwritten Whiteboard Notes
TLDR
An on-line recognition system for handwritten texts acquired from a whiteboard that uses state-of-the-art normalization and feature extraction strategies to transform a handwritten text line into a sequence of feature vectors and significantly increases the word recognition rate. Expand
Novel Hybrid NN/HMM Modelling Techniques for On-line Handwriting Recognition
TLDR
It is shown that enhancing the feature vector has only a limited effect on the standard HMMs, but a significant influence to the hybrid systems, and with an enhanced feature vector the two hybrid models highly outperform all baseline models. Expand
Offline grammar-based recognition of handwritten sentences
TLDR
This paper proposes a sequential coupling of a hidden Markov model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using stochastic context-free grammars extracted from a text corpus and concludes that syntax analysis helps to improve recognition rates significantly. Expand
...
1
2
3
4
5
...