Segmentation of Arabic Handwritten Text to Lines

In the literature, two methods for the extraction zones of the document are more used. The first method is based on the Mathematical Morphology (MM). The second is based on Hough Transform (HT). The main contribution of this paper is the application of these methods to extract the handwritten components of the complex document. The second contribution is the combination between the HT and the MM. The third contribution is the use of these three developed methods to automatically extract the handwritten components from CENPARMI bank check: numerical amount, literal amount and date zone. We present a concept for automatic evaluation of the results, based on a label tools for the different part of the used documents. We achieve a correct extraction rate of 98.27% for numerical amount, 91.82% for literal amount, and 99.63% for date, extracted by hybrid method HT-MM.

2016

The greatest difficulty facing the recognition of Arabic handwritten words is segmentation, because Arabic handwriting is cursive with complex multi-form styles. Hence, intensive research efforts are needed to reach an effective Arabic handwriting segmentation system. This paper presents a system which uses morphological features of the Arabic characters for segmentation. The proposed system segments non-overlapped (horizontally connected -e.g. "حسن") as well as overlapped (vertically connected - e.g. "نجد") characters. The result is not very good one. However, it arrives at good directives for more research. As the writing was freely without any restrictions, both over-segmentation and under-segmentation problems affect the system.

International Journal of Advanced Computer Science and Applications

Character segmentation in Unconstrained Arabic handwriting is a complex and challenging task due to the overlapping and touching of words or letters. Such issues have not been widely investigated in the literature. Addressing these issues in the segmentation stage reduces errors in the segmentation process, which plays a significant role in enhancing the accuracy of the Arabic optical character recognition. Therefore, this paper proposes a hybrid approach to improve the accuracy for interconnection, overlapping or touching character segmentation. The proposed method includes several stages: removing extra shapes such as signatures from the document. Using morphological operations, connected components and bounding box detection, detect and extract individual words directly from the document. Finally, the touching characters segmentation is achieved based on background thinning and computational analysis of the word's region. The proposed method has been tested on KHATT, IFN/ENIT database and our own collected dataset. The experimental results showed that the proposed method obtained high performance and improved the accuracy compared to other methods.

Lecture Notes in Computer Science, 2014

Text segmentation is an essential pre-processing stage for many systems such as text recognition and word spotting. However, few methods have been published for Arabic text segmentation. In Arabic handwritten documents, separating text into words is challenging due to the enormous different Arabic handwriting styles. In this paper, we present a new segmentation methodology of an Arabic handwritten text line into words. Our proposed approach of text segmentation utilizes the knowledge of Arabic writing characteristics. This method shows promising results.

Automatic off-line Arabic handwriting recognition still faces a big challenges. Due to the cursive nature of the Arabic language, most of published works are based on recognition of a whole word without segmentation. This paper presents a new framework for the recognition of handwritten Arabic words based on segmentation. This framework involves two phases (training phase and testing phase). In the training phase, Arabic handwritten characters were trained to be recognized, while in the testing phase, words were segmented into characters for recognition. Classification is achieved in two steps (classification of the segmented characters and classification of the word). A dictionary is constructed and used to correct any errors occurring during the previous stages of the recognition process. This work has been tested with IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.

Pattern Analysis & Applications

Line segmentation from unconstrained handwritten document is a difficult task because of the writing styles of different individuals. Characters of two consecutive text lines may touch or overlap and such touching/overlapping makes the line segmentation task more complex. In this paper, a painting scheme is proposed to facilitate unconstrained handwritten text-line segmentation process. In the proposed scheme, input text page is vertically decomposed into parallel pipe structures called as strip. The width of strips is automatically computed based on the space (gap) between the consecutive lines in each text-page. Each row of a strip is painted by a gray intensity, which is the average intensity value of gray values of all pixels present in that row-strip. The painted strips are then converted into two-tone painting image and using some smoothing operations the two-tone painted image is smoothed. The white/black spaces in each pipe of the smoothed image are analyzed to get a short line of separation, called as Piece-wise Potential Separating Line (PPSL), between two consecutive black spaces. Finally, the PPSLs are concatenated or extended for text-line separation. The proposed method can also handle touching/overlapping cases. To do so, the proposed system initially detects the touching/ overlapping zones and then based on the structural behavior of such zones, they are segmented.

Int. Arab J. Inf. Technol., 2017

Handwriting recognition is an important field as it has many practical applications such as for bank cheque processing, post office address processing and zip code recognition. Most applications are developed exclusively for Latin characters. However, despite tremendous effort by researchers in the past three decades, Arabic handwriting recognition accuracy remains low because of low efficiency in determining the correct segmentation points. This paper presents an approach for character segmentation of unconstrained handwritten Arabic words. First, we seek all possible character segmentation points based on structural features. Next, we develop a novel technique to create several paths for each possible segmentation point. These paths are used in differentiating between different types of segmentation points. Finally, we use heuristic rules and neural networks, utilizing the information related to segmentation points, to select the correct segmentation points. For comparison, we app...

Computer Science Department, Faculty of Computer …, 2004

Character segmentation is a crucial step in most Arabic optical text recognition systems. The recognition process depends mainly on the accuracy of the character segmentation. This paper presents a novel adaptive algorithm for the off-line segmentation of printed Arabic text. There are many challenging features in the Arabic writing, for example, it is cursive and characters in a word can take four different shapes. A general rule cannot apply for segmenting all the characters. Hence, we propose an adaptive rule-based segmentation algorithm based on the general structural relationship of the Arabic text. The main rule used is that "most characters start with and end before a T-junction on the baseline." Some other rules are included to take care of the variations in the main rule. Results show the efficiency of the proposed algorithm, where it is found to achieve a segmentation accuracy of 96.5%.

Indonesian Journal of Electrical Engineering and Computer Science, 2022

Text line extraction from a text document image and segmenting it into isolate words and segmenting these words into individual characters are considered as one of the most critical processes in optical character recognition (OCR) systems development and turning the document into a searchable electronic representation, this paper presents a new approach to analyze the Arabic text documents, the proposed approach contains four steps, preprocessing, text line segmentation, word segmentation, character segmentation. The horizontal projection method are used to detect and extract the text line from preprocessed text documents image, in word segmentation step. The space threshold are computed to determine the spaces among connected components in text line as within-word space or between-words space for segmenting the text line into isolate words, finally thinning method applied to find the skeleton of segmented word and analyses geometric characteristics of the characters to detect ligatures and characters. The proposed approach was tested and evaluated on a set of 115 text images, this set contains images from the King Fahd University of Petroleum and Minerals (KFUPM) handwritten Arabic text (KHATT) database and some images produced by the authors. The experiment results are extremely encouraging, with a success rate of 98.6% for lines segmentation, 96% for words segmentation, and 87.1% for characters segmentation.

2009 10th International Conference on Document Analysis and Recognition, 2009

In this paper, we introduce an on-line Arabic handwritten recognition system based on new stroke segmentation algorithm. The proposed algorithm uses an over segmentation method that has the advantage of giving all correct segments at least. It is based on arbitrary segmentation followed by segmentation enhancement, consecutive joints connection and finally segmentation point locating. The proposed system gives an excellent recognition rate up to 97% and 92% for words and letter recognition.