Statistical geometric components of straight lines (SGCSL) feature extraction method for offline Arabic/Persian handwritten words recognition

Tavoli, Reza; Keyvanpour, Mohammad Reza; Mozaffari, Saeed

doi:10.1049/iet-ipr.2017.0839

Cited by 21 publications

(13 citation statements)

References 28 publications

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“…In addition, a puzzle algorithm is added as a post-processor. Algorithm 4 [7] and algorithm 5 [8] are based on holistic recognition. Algorithm 4 proposes an off-line handwritten Arabic recognition algorithm based on asynchronous multi-stream hidden Markov model (HMM), which models the interaction between multiple features composed of a combination of statistical and structural features, which are extracted over the columns and rows of the word image using a sliding window approach.…”

Section: Word Recognition Resultsmentioning

confidence: 99%

“…Studies have been carried out on the recognition of handwritten Arabic words using a [10]. In addition, algorithms based on multi-stream hidden Markov model (HMM) [7], support vector machine (SVM) classifier [8], and multi-classifier fusion [9] have also achieved good results in off-line handwritten Arabic word recognition. The holistic approach performs well when the lexicon is predefined, fixed and small in size.…”

Section: Word Recognitionmentioning

confidence: 99%

“…Currently, the two dominant strategies for recognizing cursive words can be divided according to whether character segmentation is conducted [6], i.e., holistic word recognition [7][8][9][10] and segmentation-driven recognition [11][12][13]. Algorithms that conduct training and recognition of words as a whole are relatively simple, but their ability to discriminate small differences between similar words is comparatively poor.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Off-line Handwritten Uyghur Words Using Bayesian Networks with Grapheme Nodes

2020

SN COMPUT. SCI.

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This study proposes a new algorithm that constructs a word Bayesian network (BN) framework with grapheme nodes to recognize off-line handwritten Uyghur words. First, we build an Uyghur script grapheme library according to the rules and morphological structure of Uyghur. The library includes main grapheme, affix grapheme, and dot grapheme categories. Second, word images are segmented into grapheme sequences by subjecting the individual strokes to extraction, segmentation, and clustering operations. Then we design specific feature extractors and classifiers for specific graphemes to detect and identify small differences between similar words. Finally, we construct a hierarchical matching model for graphemes, conjoined segments, and words using a discrete BN. The BN infers word categories from grapheme features, calculates the confidence of inference, and integrates the grapheme recognition information and word-formation prior information to obtain the final word recognition results. A word recognition rate of 91.65% is obtained during experiments conducted with a database consisting of 12,500 samples and a total of 58 trained grapheme categories. These results indicate that the proposed algorithm not only provides a high word recognition rate by effectively avoiding character over-segmentation errors, but also employs a small and fully predictable number of training categories, which facilitates strong expansibility.

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Section: Word Recognition Resultsmentioning

confidence: 99%

Section: Word Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recognition of Off-line Handwritten Uyghur Words Using Bayesian Networks with Grapheme Nodes

2020

SN COMPUT. SCI.

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“…Then, Principle Component analysis (PCA) is applied to reduce Scale-Invariant Feature Transform (SIFT) descriptors to 64-D vectors. In a study by [8], a holistic group approach also has been applied in generating novel features for recognition of Persian/Arabic handwritten words. The generated feature is proposed based on a geometric attribute of components forming the word.…”

Section: Related Workmentioning

confidence: 99%

“…The generated feature is proposed based on a geometric attribute of components forming the word. The number, angle, location and size of a line are the parameters that represent the features in [8].…”

Section: Related Workmentioning

confidence: 99%

Triangle Shape Feature based on Selected Centroid for Arabic Subword Handwriting

Arbain¹,

Azmi²,

Kamilah³

et al. 2018

ijacsa

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Features are normally modelled based on color, texture and shape. However, some features may have different constraints based on types, styles and pattern of an image. The Arabic subword handwriting, for example, cannot be recognized by color and not suitable to be characterized based on texture. Therefore, features based on shape are suitable to be used for recognizing Arabic subword handwriting since each of the character has various characteristics such as diacritics, thinning and strokes. These characteristics can contribute to particular a shape that is unique and can represent Arabic subword handwriting. Currently, geometry shape such as triangle has been adopted to extract useful features based on triangle properties without implicating any triangle form. In order to increase classification accuracy, these properties have been categorized into several zones where the number of features produced is directly proportional to the number of zones. Nevertheless, shape representation does not implicate any triangle properties such as ratio of side, angle and gradient. By using shape representation, it helps in reducing the number of features. Thus, this paper presents feature based on triangle shape that can represent the identity of Arabic subword handwriting. The method based on triangle shape identifies three main coordinates of triangle formed based on selected centroids. The AHDB dataset is used as a testing data. The Support Vector Machine (SVM) and Random Forest (RF), respectively were used to measure the accuracy of the proposed method using triangle shape as a feature. The accuracy results have shown better outcome with 77.65% (SVM) and 76.43% (RF), which prove the feature based on triangle shape is applicable for Arabic subword handwriting recognition.

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Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks

Zohrevand

Sattari

Sadri

et al. 2020

Pattern Recognition and Artificial Intelligence

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Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient.

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Statistical geometric components of straight lines (SGCSL) feature extraction method for offline Arabic/Persian handwritten words recognition

Cited by 21 publications

References 28 publications

Recognition of Off-line Handwritten Uyghur Words Using Bayesian Networks with Grapheme Nodes

Recognition of Off-line Handwritten Uyghur Words Using Bayesian Networks with Grapheme Nodes

Triangle Shape Feature based on Selected Centroid for Arabic Subword Handwriting

Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks

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