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Title
Abstract
Figures
Introduction
Slope/Slant/Baseline Existing Methods
Experimental and Discussion
Databases
Experimental Results
References

Arabic handwritten: pre-processing and segmentation

Profile image of Naseer AljawadNaseer Aljawad

2012, Mobile Multimedia/Image Processing, Security, and Applications 2012

https://doi.org/10.1117/12.917555
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8 pages

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Abstract

This paper is concerned with pre-processing and segmentation tasks that influence the performance of Optical Character Recognition (OCR) systems and handwritten/printed text recognition. In Arabic, these tasks are adversely effected by the fact that many words are made up of sub-words, with many sub-words there associated one or more diacritics that are not connected to the sub-word's body; there could be multiple instances of sub-words overlap. To overcome these problems we investigate and develop segmentation techniques that first segment a document into sub-words, link the diacritics with their sub-words, and removes possible overlapping between words and sub-words. We shall also investigate two approaches for pre-processing tasks to estimate sub-words baseline, and to determine parameters that yield appropriate slope correction, slant removal. We shall investigate the use of linear regression on sub-words pixels to determine their central x and y coordinates, as well as their high density part. We also develop a new incremental rotation procedure to be performed on sub-words that determines the best rotation angle needed to realign baselines. We shall demonstrate the benefits of these proposals by conducting extensive experiments on publicly available databases and in-house created databases. These algorithms help improve character segmentation accuracy by transforming handwritten Arabic text into a form that could benefit from analysis of printed text.

Figures (10)
Arabic Handwritten Optical Character Recognition (AHOCR) is a challenging task in computer pattern recognition and image processing. AHOCR have a variety of applications including: digitising historic archives, security, authentication [1] [2], forensic [3], and more. In such applications, however, recognising words and sentences are the main objectives. Traditionally, documents are first segmented into lines, words, and then characters, using labelling connected component strategy. Words in Arabic, Farsi, Urdu, Kurdish, and other simi Some characters can be connected with its neighbours on one or ar languages consist of different types of characters. both sides, while other characters may be completely disconnected from its neighbours. Indeed, words in such languages are aggregation of sub-words that consist of one or more characters. Moreover, some characters may have a diacritic that distinguish it from characters of the same shape or the same pronunciation. The shape of the diacritic can be either small dot or small symbol. In printed texts, this structure makes a word consist of multiple sub-words separated by narrow s paces [4], as shown in Figure 1. Figure 1: Printed Arabic Statement: (a) Words are indicated by overlines, (b) Sub-words are underlined In printed or handwritten text, there are two approaches to OCR: holistic or segmentation-based. In holistic approaches words/sub-words are recognised as a whole without segmenting them into characters [5]. In a segmentation-based approach, characters are recognised individually after extraction from words or sub-words [4]. OCR problems are dealt with using a list of known procedures: pre-processing, segmentation, feature extraction, recognition, and post
Arabic Handwritten Optical Character Recognition (AHOCR) is a challenging task in computer pattern recognition and image processing. AHOCR have a variety of applications including: digitising historic archives, security, authentication [1] [2], forensic [3], and more. In such applications, however, recognising words and sentences are the main objectives. Traditionally, documents are first segmented into lines, words, and then characters, using labelling connected component strategy. Words in Arabic, Farsi, Urdu, Kurdish, and other simi Some characters can be connected with its neighbours on one or ar languages consist of different types of characters. both sides, while other characters may be completely disconnected from its neighbours. Indeed, words in such languages are aggregation of sub-words that consist of one or more characters. Moreover, some characters may have a diacritic that distinguish it from characters of the same shape or the same pronunciation. The shape of the diacritic can be either small dot or small symbol. In printed texts, this structure makes a word consist of multiple sub-words separated by narrow s paces [4], as shown in Figure 1. Figure 1: Printed Arabic Statement: (a) Words are indicated by overlines, (b) Sub-words are underlined In printed or handwritten text, there are two approaches to OCR: holistic or segmentation-based. In holistic approaches words/sub-words are recognised as a whole without segmenting them into characters [5]. In a segmentation-based approach, characters are recognised individually after extraction from words or sub-words [4]. OCR problems are dealt with using a list of known procedures: pre-processing, segmentation, feature extraction, recognition, and post
recognition. Pre-processing consists of filtering, binarisation, line segmentation, word segmentation, slope correction, slant removal, baseline estimation, and size normalizing as shown in Figure 2. The main focus of this paper is the segmentation of Arabic handwriting text into characters. We shall investigate and develop two interleaved steps approach to deal with this problem. First and overlaps between sub-words will be eliminated. We shall adopt a y, documents will be segmented into sub-words Labelling the Connected Component algorithm (LCCA) for the sub-words segmentation. The second step, referred to as the pre-processing step, works by removing/correcting the slope/slant form the extracted sub-words to improve the chance of accurate character segmentation. For efficiency, the slope/slant correction algorithm does not wait for the entire document to be sub-word segmented. The developed algorithms form the core of our proposed AHOCR scheme, depicted by the block diagram shown in Figure 3, which is a much slim version of a traditional OCR sc heme as depicted above.
recognition. Pre-processing consists of filtering, binarisation, line segmentation, word segmentation, slope correction, slant removal, baseline estimation, and size normalizing as shown in Figure 2. The main focus of this paper is the segmentation of Arabic handwriting text into characters. We shall investigate and develop two interleaved steps approach to deal with this problem. First and overlaps between sub-words will be eliminated. We shall adopt a y, documents will be segmented into sub-words Labelling the Connected Component algorithm (LCCA) for the sub-words segmentation. The second step, referred to as the pre-processing step, works by removing/correcting the slope/slant form the extracted sub-words to improve the chance of accurate character segmentation. For efficiency, the slope/slant correction algorithm does not wait for the entire document to be sub-word segmented. The developed algorithms form the core of our proposed AHOCR scheme, depicted by the block diagram shown in Figure 3, which is a much slim version of a traditional OCR sc heme as depicted above.
The rest of this paper is organised as follows; Section 2 covers the segmentation stage where overlapping between sub words is discussed, section 3 discusses the pre-processing stage where the slope/slant removal are performed, in sectio1 4, the experiments will be discussed, and finally, the character segmentation, conclusions will be the last step of thi: paper. For the benefit of the non-Arabic readers, we will present the printed version of the handwritten texts.
The rest of this paper is organised as follows; Section 2 covers the segmentation stage where overlapping between sub words is discussed, section 3 discusses the pre-processing stage where the slope/slant removal are performed, in sectio1 4, the experiments will be discussed, and finally, the character segmentation, conclusions will be the last step of thi: paper. For the benefit of the non-Arabic readers, we will present the printed version of the handwritten texts.
The naive regression algorithm for rotating a sub-word image is based on the use the slope of the regression line of pixels, i.e. slope = tan(6), where 6=tan-1(slope) is the angle shown in Figure 6 . This is illustrated in Figure 7
The naive regression algorithm for rotating a sub-word image is based on the use the slope of the regression line of pixels, i.e. slope = tan(6), where 6=tan-1(slope) is the angle shown in Figure 6 . This is illustrated in Figure 7
Figure 7: Naive regression algorithm A. Printed sub-word, B. The original sloped Sub-word, and C Rotated handwritter sub word image by using linear regression equation.
Figure 7: Naive regression algorithm A. Printed sub-word, B. The original sloped Sub-word, and C Rotated handwritter sub word image by using linear regression equation.
Figure 8: No-tails Regression algorithm. (a) and (b) printed sub-words. (d) and (e) example of sloped sub-words. (c) Projection of (d). Circled areas are tails. B: (a) and (b) after slope removal, by using linear regression after cancelling tails. Figure 7, demonstrate benefits of using the naive regression algorithm but it is not suitable for some sub-words, especially when we have long ascenders or descenders (upper or lower tails) as shown in Figure 8A. Indeed the experimental results given in Table 1, below, demonstrate the shortcoming of this algorithm. To achieve better results, we should first remove tails and only then calculate linear regression for the remaining part. Tails are easily determined from the sub-word horizontal projections as illustrated in Figure 8A (d). The angle defined by the slope of the no-tails regression line can be used to rotate the original sub-word, see Figure 8B. This modification of the naive regression algorithm will be referred to as the No-tails Regression algorithm and experimental results shown in Table 1, below, demonstrate significantly improved results but there are still more room for improvement.
Figure 8: No-tails Regression algorithm. (a) and (b) printed sub-words. (d) and (e) example of sloped sub-words. (c) Projection of (d). Circled areas are tails. B: (a) and (b) after slope removal, by using linear regression after cancelling tails. Figure 7, demonstrate benefits of using the naive regression algorithm but it is not suitable for some sub-words, especially when we have long ascenders or descenders (upper or lower tails) as shown in Figure 8A. Indeed the experimental results given in Table 1, below, demonstrate the shortcoming of this algorithm. To achieve better results, we should first remove tails and only then calculate linear regression for the remaining part. Tails are easily determined from the sub-word horizontal projections as illustrated in Figure 8A (d). The angle defined by the slope of the no-tails regression line can be used to rotate the original sub-word, see Figure 8B. This modification of the naive regression algorithm will be referred to as the No-tails Regression algorithm and experimental results shown in Table 1, below, demonstrate significantly improved results but there are still more room for improvement.
Figure 9: A: Regression in a vertical box: (a) Printed sub-word, (b) Sloped sub-word , (c) vertical boxes, (d) slope removal result. B:Regression in a horizontal box: (a) Printed sub-word (b). Sloped sub-word (c). horizontal box. C: Regression of the highest de
Figure 9: A: Regression in a vertical box: (a) Printed sub-word, (b) Sloped sub-word , (c) vertical boxes, (d) slope removal result. B:Regression in a horizontal box: (a) Printed sub-word (b). Sloped sub-word (c). horizontal box. C: Regression of the highest de
Figure 10: A: Printed sub-word, B: The HHP algorithm, and C: The HTP algorithm. All sub-words are with their horizontal and vertical projection.
Figure 10: A: Printed sub-word, B: The HHP algorithm, and C: The HTP algorithm. All sub-words are with their horizontal and vertical projection.
Table 1. Experimental Results
Table 1. Experimental Results
Figure 11: A:(a). Arabic Printed Words, (b). Arabic Handwritten Words, (c).Slope/Slant Removal then character segmentation. The Rectangle areas are faulty segmentation. B:(a). Arabic Handwritten Words, (b). segmentation words to sub-words by using LCCA,(c) Slant Removal followed by character segmentation Clearly segmenting sub-word without solving overlapping problem, will definitely lead to faulty characters segmentatio: as shown in Figure 11A.
Figure 11: A:(a). Arabic Printed Words, (b). Arabic Handwritten Words, (c).Slope/Slant Removal then character segmentation. The Rectangle areas are faulty segmentation. B:(a). Arabic Handwritten Words, (b). segmentation words to sub-words by using LCCA,(c) Slant Removal followed by character segmentation Clearly segmenting sub-word without solving overlapping problem, will definitely lead to faulty characters segmentatio: as shown in Figure 11A.

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