A Front-End OCR for Omni-Font Persian/Arabic Cursive Printed Documents

Mehran, Ramin; Pirsiavash, Hamed; Razzazi, Farbod

doi:10.1109/dicta.2005.3

Cited by 21 publications

(5 citation statements)

References 15 publications

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“…As is seen, the values of the horizontal coordinates are from 1 to 42. The numbers in the range of (1)(2)(3)(4)(5)(6) in the horizontal coordinates are symbols for the connected components with sizes 1 × 1, 1 × 2, 1 × 3, 1 × 4, 1 × 5, and 1 × 6; and the numbers in the range of (7)(8)(9)(10)(11)(12) are symbols for the connected components with sizes 2 × 1, 2 × 2, 2 × 3, 2 × 4, 2 × 5, and 2 × 6, respectively.…”

Section: Font Recognition and Keyword Modificationmentioning

confidence: 99%

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A word spotting method for Farsi machine-printed document images

Pourasad¹,

Hassibi²,

Ghorbani³

2013

Turkish Journal of Electrical Engineering and Computer Sciences

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Section: Font Recognition and Keyword Modificationmentioning

confidence: 99%

“…In keyword spotting methods, searching is done in the image domain without converting to text. Although there have been great attempts in producing OCR systems for the Farsi/Arabic language, such as those in [1,2], the overall performances of such systems are far from perfect.…”

Section: Introductionmentioning

confidence: 99%

A word spotting method for Farsi machine-printed document images

Pourasad¹,

Hassibi²,

Ghorbani³

2013

Turkish Journal of Electrical Engineering and Computer Sciences

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“…The most important of these disadvantages is that it costs a lot in converting huge amounts of documents and also it is not sufficiently successful in applying it on low quality texts and documents with complicated layout. Additionally, there is no robust OCR method available yet for Farsi language scripts [2,3]. In order to overcome these problems, researchers suggested another method for document image retrieval that is called keyword spotting or, more simply, word spotting [4].…”

Section: Introductionmentioning

confidence: 99%

“…The locative layout structure of a word image and classification of components of its layout have useful information for recognizing Farsi words. According to a literature review above and considering the method discussed in [2,3], in this paper, we propose a new model for machineprinted Farsi text retrieval based on the similarities of layout of components in Farsi words. The new method is actually the implementation of the method proposed in [28,29].…”

Section: Introductionmentioning

confidence: 99%

Farsi document image recognition system using word layout signature

Ergun¹,

Norozpour²

2019

Turk J Elec Eng & Comp Sci

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In this paper, a new representation of Farsi words is proposed to present the keyword spotting problems in Farsi document image retrieval. In this regard, we define a signature for each Farsi word based on the word connected component layout. The mentioned signature is shown as boxes, and then, by sketching vertical and horizontal lines, we construct a grid of each word to provide a new descriptor. One of the advantages of this method is that it can be used for both handwritten and machine-printed texts. Finally, to evaluate the performance of our system in comparison to other methods, a database that contains 19,582 printed Farsi words is examined, and after applying this approach, a recall rate of 98.1% and a precision rate of 94.3% are obtained.

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“…Most of retrieval and recognition methods are divided into two category [1]: The first category methods retrieval and recognation document images based on description of global shape of words or sub-words. In this method the descriptor are directly extracted from the image of the word or subword [1,3,4,9,10,15]. The second category methods segment a word to its letters and then extract features from the image of each letter.…”

Section: Introductionmentioning

confidence: 99%

Farsi/Arabic Document Image Retrieval Through Sub-Letter Shape Coding

Bahmani

Azmi

2011

International Conference on Computer Technology and Development, 3rd (ICCTD 2011)

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A retrieval method for explicit recognition free Farsi/Arabic document is proposed in this paper. The system can be used in mixed Farsi/Arabic and English text. The method consists of Preprocessing, word and sub_word extraction, detection and cancelation of sub_letter connectors, annotation sub_letters by shape coding, classifier of sub_letters by use of decision tree and using of RBF neural network for sub_letter recognition. The Proposed system retrieves document images by a new sub_letter shape coding scheme in Farsi/Arabic documents. In this method document content captures through sub_letter coding of words. The decision tree-based classifier partitions the sub_letters space into a number of sub regions by splitting the sub_letter space, using one topological shape features at a time. Topological shape Features include height, width, holes, openings, valleys, jags, sub_letter ascenders/descanters. Experimental results show advantages of this method in Farsi/Arabic Document Image Retrieval.

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A Front-End OCR for Omni-Font Persian/Arabic Cursive Printed Documents

Abstract: Abstract

Cited by 21 publications

References 15 publications

A word spotting method for Farsi machine-printed document images

A word spotting method for Farsi machine-printed document images

Farsi document image recognition system using word layout signature

Farsi/Arabic Document Image Retrieval Through Sub-Letter Shape Coding

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