Statistical Machine Translation as a Language Model for Handwriting Recognition

Devlin, Jacob; Kamali, Matin; Subramanian, Krishna; Prasad, Rohit; Natarajan, Prem

doi:10.1109/icfhr.2012.273

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Furthermore, in certain document collections, several languages are used, sometimes even in the same paragraph. Corpora for transcription systems for contemporary texts usually contain millions of words gathered from various sources [21,22], which we can not provide for the bootstrapping of handwriting recognition for the document collections in Monk.…”

Section: Discussionmentioning

confidence: 99%

Separability versus prototypicality in handwritten word-image retrieval

Oosten

Schomaker

2014

Pattern Recognition

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Section: Discussionmentioning

confidence: 99%

Separability versus prototypicality in handwritten word-image retrieval

Oosten

Schomaker

2014

Pattern Recognition

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“…Highly advanced Artificial Intelligence (AI), along with its associated sub-disciplines like machine learn-ing (ML), paved the way for a wide range of real-world applications in sectors such as visual recognition systems, language understanding, automated translation techniques, and robotic innovations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In recent years, the most prominent sub-field of machine learning (ML), Artificial Neural Networks (ANNs), have been widely and effectively utilized to transform technological advancements and elevate both businesses and daily life towards a new stage of AI sophistication [19][20][21][22][23][24][25].…”

Section: Generalmentioning

confidence: 99%

Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner

Michailidis

Gkelios

et al. 2023

ICA

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Distributed Machine learning has delivered considerable advances in training neural networks by leveraging parallel processing, scalability, and fault tolerance to accelerate the process and improve model performance. However, training of large-size models has exhibited numerous challenges, due to the gradient dependence that conventional approaches integrate. To improve the training efficiency of such models, gradient-free distributed methodologies have emerged fostering the gradient-independent parallel processing and efficient utilization of resources across multiple devices or nodes. However, such approaches, are usually restricted to specific applications, due to their conceptual limitations: computational and communicational requirements between partitions, limited partitioning solely into layers, limited sequential learning between the different layers, as well as training a potential model in solely synchronous mode. In this paper, we propose and evaluate, the Neuro-Distributed Cognitive Adaptive Optimization (ND-CAO) methodology, a novel gradient-free algorithm that enables the efficient distributed training of arbitrary types of neural networks, in both synchronous and asynchronous manner. Contrary to the majority of existing methodologies, ND-CAO is applicable to any possible splitting of a potential neural network, into blocks (partitions), with each of the blocks allowed to update its parameters fully asynchronously and independently of the rest of the blocks. Most importantly, no data exchange is required between the different blocks during training with the only information each block requires is the global performance of the model. Convergence of ND-CAO is mathematically established for generic neural network architectures, independently of the particular choices made, while four comprehensive experimental cases, considering different model architectures and image classification tasks, validate the algorithms’ robustness and effectiveness in both synchronous and asynchronous training modes. Moreover, by conducting a thorough comparison between synchronous and asynchronous ND-CAO training, the algorithm is identified as an efficient scheme to train neural networks in a novel gradient-independent, distributed, and asynchronous manner, delivering similar – or even improved results in Loss and Accuracy measures.

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“…OCR is utilised to recognise languages of different origins not only in Roman script [42] but also in the case of degraded language documents. Another use of OCR to recognise handwriting is presented in [43], where the authors used the n-gram model, which produces groups of words that overlay on each other. The authors extended such model and utilised the likelihood score, from a statistical machine translation system, as main feature, so as to be able to capture the image of the words and to translate each image into other languages.…”

Section: Optical Character Recognitionmentioning

confidence: 99%

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021

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Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

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Statistical Machine Translation as a Language Model for Handwriting Recognition

Cited by 9 publications

References 11 publications

Separability versus prototypicality in handwritten word-image retrieval

Separability versus prototypicality in handwritten word-image retrieval

Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

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