Farsi Font Recognition Using Holes of Letters and Horizontal Projection Profile

Pourasad, Yaghoub; Hassibi, Houshang; Ghorbani, Azam

doi:10.1007/978-3-642-27337-7_21

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…The fonts used for each comparison are shown also in Table 1. From the mean recognition rate in Table 1, it is clear that the proposed algorithm outperforms the other algorithms in [2][3][4][5][6][7][8]. The miss classification in our algorithm occurs when trying to find the font type of a page that has very few written words.…”

Section: Results and Performance Analysismentioning

confidence: 93%

“…The proposed algorithm uses scale-invariant detector, gradient-based descriptor, and k-means clustering to recognize the Arabic font in text image. The proposed algorithm shows a promising performance, and it produces a mean recognition rate of 99.2-99.5 % and outperforms the algorithms in [2][3][4][5][6][7][8] that are used for AOFR.…”

Section: Resultsmentioning

confidence: 98%

“…This comparison was carried out using (ACS Fayrouz Bold, ACS Morgan Bold, AF_El Khobar, AGA Cairo Regular, AlKharashi 27, Andalus, Arabic Transparent, B Homa, Droid Arabic Naskh, Kufi, B Zar, DecoType Thuluth, M Unicode The second test phase is to compare the performance of the proposed algorithm with that of the state-of-the-art algorithms in the field of AOFR. Table 1 shows the mean recognition rate for the proposed algorithm and the algorithms in [2][3][4][5][6][7][8]. The results of the proposed algorithm in each row of Table 1 were obtained using the same set of fonts used in the paper that it is compared with.…”

Section: Results and Performance Analysismentioning

confidence: 99%

“…In [6,7], features from the holes of the letters and the horizontal profile projection of the text lines are used. These algorithms work at the text line images level to identify the font type.…”

Section: Optical Font Recognitionmentioning

confidence: 99%

See 3 more Smart Citations

An efficient algorithm for Arabic optical font recognition using scale-invariant detector

Mouśa

Sayed

Abdalla

2015

IJDAR

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This paper proposes a new algorithm for Arabic optical font recognition (AOFR) as the first stage for Arabic optical character recognition. The proposed algorithm uses scale-invariant detector, gradient-based descriptor, and k-means clustering. The scale-invariant detector is used to find key points that identify the font of an image of printed Arabic text. The work in this paper compares between several scale-invariant detectors and selects the best one for AOFR. A gradient-based descriptor similar to the one in the famous scale-invariant feature transform algorithm is used to describe the detected key points. In addition, k-means clustering is used for font classification. In this paper, the mean recognition rate is used to evaluate the performance of the proposed algorithm. The proposed algorithm shows superior performance when compared with recently published algorithms for AOFR. B Mohammed S. Sayed

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Section: Results and Performance Analysismentioning

confidence: 93%

Section: Resultsmentioning

confidence: 98%

Section: Results and Performance Analysismentioning

confidence: 99%

Section: Optical Font Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

An efficient algorithm for Arabic optical font recognition using scale-invariant detector

Mouśa

Sayed

Abdalla

2015

IJDAR

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“…In [25], wavelet transform and neural network were used for feature extraction and classification respectively, to detect Arabic fonts. In [26], stack and points were used to extract features for detecting seven fonts. In [27], the holes in the characters and horizontal projection profile of text lines were used to extract features in detecting Farsi font.…”

Section: -1-related Workmentioning

confidence: 99%

Farsi Font Detection using the Adaptive RKEM-SURF Algorithm

Hossein-Nejad¹,

Agahi²,

Mahmoodzadeh³

2020

jist

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Farsi font detection is considered as the first stage in the Farsi optical character recognition (FOCR) of scanned printed texts. To this aim, this paper proposes an improved version of the speeded-up robust features (SURF) algorithm, as the feature detector in the font recognition process. The SURF algorithm suffers from creation of several redundant features during the detection phase. Thus, the presented version employs the redundant keypoint elimination method (RKEM) to enhance the matching performance of the SURF by reducing unnecessary keypoints. Although the performance of the RKEM is acceptable in this task, it exploits a fixed experimental threshold value which has a detrimental impact on the results. In this paper, an Adaptive RKEM is proposed for the SURF algorithm which considers image type and distortion, when adjusting the threshold value. Then, this improved version is applied to recognize Farsi fonts in texts. To do this, the proposed Adaptive RKEM-SURF detects the keypoints and then SURF is used as the descriptor for the features. Finally, the matching process is done using the nearest neighbor distance ratio. The proposed approach is compared with recently published algorithms for FOCR to confirm its superiority. This method has the capability to be generalized to other languages such as Arabic and English.

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