Handwritten word-spotting using hidden Markov models and universal vocabularies

Rodríguez-Serrano, José A.; Perronnin, Florent

doi:10.1016/j.patcog.2009.02.005

Cited by 147 publications

(68 citation statements)

References 29 publications

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“…holistic approaches that model word images with HMMs [47]. For comparison with our NN-based word spotting system, we have applied DTW to perfectly segmented word images, i.e.…”

Section: Reference Systemsmentioning

confidence: 99%

“…It is based on Hidden Markov Models (HMMs). HMMs are state-of-the-art for modeling handwritten text [51] and have been widely used for keyword spotting [3], [28], [31], [33], [47], [52], [53]. In [30], trained character models are used to spot arbitrary keywords in complete text line images using an efficient lexicon-free approach.…”

Section: B Hmm Reference Systemmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Word Spotting Method Based on Recurrent Neural Networks

Frinken

Fischer

Manmatha

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

242

191

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Abstract-Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e. it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperforms not only a classical dynamic time warping based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

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“…holistic approaches that model word images with HMMs [47]. For comparison with our NN-based word spotting system, we have applied DTW to perfectly segmented word images, i.e.…”

Section: Reference Systemsmentioning

confidence: 99%

Section: B Hmm Reference Systemmentioning

confidence: 99%

A Novel Word Spotting Method Based on Recurrent Neural Networks

Frinken

Fischer

Manmatha

et al. 2012

IEEE Trans. Pattern Anal. Mach. Intell.

242

191

View full text Add to dashboard Cite

show abstract

“…Different methods have been suggested for efficient retrieval, word spotting is popular for recovering the relevant images without recognition. Various word spotting techniques have been used, such as the Dynamic Time Warping (DTW) algorithm [3], Hidden Markov Model (HMM) [2] etc. DTW compares a series of feature vector for image retrieval, though successful in many cases it has a drawback when it comes to large databases, having a computation time of 1 second for comparing two word images hence it loses its practicality.…”

Section: Introductionmentioning

confidence: 99%

Inscription Image Retrieval Using Bag of Visual Words

Jayanthi

Indu

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper presents a technique for efficient and veracious retrieval of ancient inscriptions and manuscripts from a large database of images by using the Bag of Visual Words (BoVW) technique. The proposed method can be used to recognize inscription images across the world. SURF (speeded up robust features) is used as an image feature extractor. A visual vocabulary is created by representing the image as a histogram of visual words which helps in the retrieval process. Usage of SURF ensures scalability, faster processing better results with darkened and blurred images. We demonstrate the method on a combination of 300 inscriptions images comprising of several inscription around the world.

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“…Within this paradigm, there are the so-called lexicon-free methods, which as their name indicate, don't relay on a predefined vocabulary and therefore allow users to search for any wanted query. As examples of lexicon-free KWS approaches for handwritten documents, we can mention the popular ones based on Hidden Markov Model filler (HMM-Filler) [1], [2], [3] or those based on recurrent neural networks (RNNs) [4], [5].…”

Section: Introductionmentioning

confidence: 99%