Arabic Handwriting Recognition Using Variable Duration HMM

Kundu, A.; Hines, T.; Phillips, Jon; Huyck, Benjamin; Guilder, L.C. Van

doi:10.1109/icdar.2007.4376994

Cited by 19 publications

(9 citation statements)

References 5 publications

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“…The approach used combines density based features and contour based features of sliding windows. In [12], each character is a state in Variable Duration Hidden Markov Models and has a variable duration to model a character model of multiple segments.…”

Section: Introductionmentioning

confidence: 99%

Segmentation-free Word Spotting for Handwritten Arabic Documents

Khaissidi¹,

Elfakir²,

Mrabti³

et al. 2016

IJIMAI

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-In this paper we present an unsupervised segmentation-free method for spotting and searching query, especially, for images documents in handwritten Arabic, for this, Histograms of Oriented Gradients (HOGs) are used as the feature vectors to represent the query and documents image. Then, we compress the descriptors with the product quantization method. Finally, a better representation of the query is obtained by using the Support Vector Machines (SVM).

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Section: Introductionmentioning

confidence: 99%

Segmentation-free Word Spotting for Handwritten Arabic Documents

Khaissidi¹,

Elfakir²,

Mrabti³

et al. 2016

IJIMAI

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“…Certain approaches have been proposed to alleviate the strict dependency of segmentation-based HWR on correctly subdividing words into the characters they consist of. As one example, explicit over-segmentation is performed (cf., e.g., [70]) and based on some alignment technique (as, e.g., dynamic programming), the optimal "segmentation path" through the word to be recognized is extracted [44,58,91,110]. Alternatively, multiple segmentation solutions are generated by variants of the segmentation technique and the "best" solution w.r.t.…”

Section: Segmentation-free Versus Segmentation-based Recognitionmentioning

confidence: 99%

“…It is, however, quite cumbersome to define a coding of the inherently numeric feature representations of offline handwriting data into a symbol set. Therefore, today, only very few approaches still make use of discrete HMMs operating on either discretely modeled distributions in feature space [70] or a hand-crafted symbolic coding of the data [3]. Very rarely, discrete symbols are combined with continuous attributes for output modeling [127].…”

Section: Modeling Output Behaviormentioning

confidence: 99%

Markov models for offline handwriting recognition: a survey

Plötz

Fink

2009

IJDAR

191

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Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-modelbased offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.

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“…Classification is performed after pre-processing step to the values of the resulting features with Error Back Propagation Artificial Neural Network (EBPANN), from this process will be obtained calculation of recognition, all the data which will then determine the percentage of success of this method [12]. The tests in our neural network consists image processing phase, feature vector extraction phase, and three network phases that is Neural network structure phase, Error Back Propagation algorithm phase, Running neural network structure .Each character is represented as a 18x18 array of binary pixels image, with 0 representing "off" and 1 representing "on".…”

Section: ) Classificationmentioning

confidence: 99%

High Accuracy Arabic Handwritten Characters Recognition Using Error Back Propagation Artificial Neural Networks

Abed¹,

Alasad²

2015

ijacsa

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Abstract-This manuscript considers a new architecture to handwritten characters recognition based on simulation of the behavior of one type of artificial neural network, called the Error Back Propagation Artificial Neural Network (EBPANN). We present an overview of our neural network to be optimized and tested on 12 offline isolated Arabic handwritten characters ‫،ش،ط،غ،ف،ك،م،و،ي(‬ ‫)ا,ت,ج،ذ‬ because the similarity of some Arabic characters and the location of the points in the character. Accuracy of 93.61% is achieved using EBPANN which is the highest accuracy achieved during Offline Handwritten Arabic Character Recognition. It is noted that the EBPANN in general generates an optimized comparison between the input samples and database samples which improves the final recognition rate. Experimental results show that the EBPANN is convergent and more accurate in solutions that minimize the error recognition rate.

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Arabic Handwriting Recognition Using Variable Duration HMM

Cited by 19 publications

References 5 publications

Segmentation-free Word Spotting for Handwritten Arabic Documents

Segmentation-free Word Spotting for Handwritten Arabic Documents

Markov models for offline handwriting recognition: a survey

High Accuracy Arabic Handwritten Characters Recognition Using Error Back Propagation Artificial Neural Networks

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