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If you manage this site and have a question about why the site is not available, please contact us directly. Jennifer Chiu is the lead Licensed Esthetician in our McDonough medispa location and our patients could not be happier. Jennifer Chiu treats our McDonough patients with the utmost care, patience and knowledge out of her over 7 years of experience in skincare and medispa services. Jennifer’s unique specialties include acne treatments for all ages, chemical peels, LED light therapy, and microdermabrasion. To round out her beauty and aesthetics education Jennifer has also been highly trained in makeup artistry. Her welcoming and bright personality in conjunction with her skilled and passionate demeanor has transformed our McDonough office into the must-try medispa for both injectable and skin care needs on Atlanta’s south side.
She works with our team and on her own to stay up to date on continuing education in the beauty industry and takes full advantage of learning new products and procedures as opportunities arise. She will often spend time with each patient prior to treatment getting a history on what has and has not worked for them in the past in regards to skin care.
This advanced knowledge allows her to not only know the best products and treatments that will help her patients get their skin back to it’s best possible condition, but she is also able to recommend best practices when patients have further questions on makeup and their current beauty routine.
She also takes time to get the know the lifestyle and habits of each of her clients so that she is best equipped and prepared to offer them the most effective treatment options and take-home products that will blend nicely into their current daily needs and routines. In this paper, a novel approach is proposed to segment handwritten Arabic text (words).
The segmentation algorithm employs seven agents in order to detect regions where segmentation is illegal. Feature points (end points) are extracted from the remaining regions of the word-image.
Initially, the middle of every two successive end points is considered as a candidate segmentation point based on a set of rules. The experimental results are very promising as we achieved a success rate of 86%. Introduction For the past three decades, there has been increasing interest among researchers in problems related to handwritten text segmentation and recognition regardless of the language used [1].
Most of the handwriting recognition systems are based on segmentation, which is the operation that seeks to decompose a word image into a sequence of sub-images containing isolated characters.
Despite of the extensive work done on the off-line recognition of handwritten Latin and Asian languages text, a small number of research papers and reports are published in the recognition of Arabic handwriting [2]. Another difficulty is the existence of overlapping characters which are not attached to each other but share horizontal space.
Due to difficulties mentioned above, many researchers bypass the segmentation stage in developing a recognition system.
However, this is not practical and insufficient in applications that require recognition of a large number of vocabularies where several words may have the same global shape, such as bank check processing, postal address and zip code recognition [3,4], automated handwritten document entry and understanding, mail sorting, and other business and scientific applications. In addition, segmentation has an effective role in reducing the complexity of recognition systems since the number of recognition classes will be the number of Arabic letters and not the possible combinations of them.
The proposed approach utilizes seven agents which cooperate to identify the regions where the insertion of segmentation points is illegal. The segmentation algorithm is described in Figure 1 as a block diagram. Each agent is responsible for detecting a specific region where segmentation is not permitted. These regions share general characteristics with Arabic letters such as loops and cavities, as shown in Table 1. After pulling out the detected regions, end-point features are extracted from the remaining regions of the word.
The center point of every two successive end points is considered as a candidate cutting point. Finally, heuristic rules are applied to verify the validity of the suggested cutting points.
Experimental results are discussed in Section 6; and conclusions and future directions are presented in Section 7. Related Work Segmentation stage is a crucial task in any character recognition system and especially Arabic handwritings recognition. Its difficulty stems from the high variability of writing scripts and styles which could be affected by writers’ health and mood.
An elaborate survey about character recognition systems is done by Amin [2].

A survey on character segmentation techniques and methods may be found in [5]. A multi-agents approach to separating handwritten connected digits is described in [6]. An overview of methods for separating handwritten characters is described in [7]. External segmentation is the most commonly used approach to segmentation, where the possible letter boundaries are found prior to recognition. In holistic based algorithms (also called no segmentation) Table 1. However, no performance results were reported for his algorithm. It finds segmentation points in printed and cursive handwritten words by looking for minimas or arcs between letters which can be the ideal segmentation points.
It is based on extracting connected block of characters and looking for topographic features to identify possible segmentation points.
The distribution of these points is based on the average character width in the block. The system achieved an accuracy of 69.72%, but two major problems were encountered.
The first problem is that the system failed in segmenting horizontal overlapping characters.
Moreover, our method is not only expected to resolve the shortcomings of the previous related methods but also to achieve better results by avoiding under segmentation.
This depends on agents that extract the appropriate features of Arabic handwritings which improve detecting candidate segmentation points. Our proposed system is a recognition-free segmenting Arabic handwritten system. The proposed system employs multi-agents in which agents cooperate to identify potential segmentation points.
In addition, heuristic rules are applied to verify the validity of the suggested cutting points. Preprocessing Phase The image of the Arabic handwritten text is first scanned and converted into a binary format.
Then, the image is cleaned by removing any noise produced by the optical scanning device or the writing instrument. The handwritten text is then segmented into lines using horizontal projection of black pixels on the Y-axis. This step is fundamental since it is needed by agents for a successful extraction of feature points. A branch point is the intersection point in letter “T”; it connects three line segments. Cross point connects four line segments, similar to the intersection point in “+” sign, rotated by different angles.
The idea behind selecting these agents stems from the fact that Arabic characters consist of two main shapes: loop and cavity, as shown in Table 1. On the other hand, the agent has a significant role in aiding the under-baseline-cavities agent to decide whether a cavity exists under the baseline of the word or not. Starting from the upper left of the image, the agent traces the word boundaries looking for continuous black pixels that enclose a loop. This process is terminated when the agent reaches the end of the word.
The extracted group(s) of continuous black pixels, if any, is returned as a loop.
The detection of under-baseline-cavities needs the assistance of baseline agent. Under-baseline-cavities agent traces the boundary of the word looking for the deepest point in the boundary. It states that the letter with such cavity occupies and shares a space with the letter proceeds it. The agent treats this problem by eliminating the right cavity part (descender) before the segmentation process starts, as shown in Figure 8. After determining the cutting points, the descendant parts are retained to their locations. Feature points are also important in detecting this region, where the agent traces the boundaries of the word searching for pixels that form a right cavity shape.

Each end point feature in the word is a candidate start of a right cavity.
Therefore, the agent restarts tracing from each end point and keeps track of pixels which construct a right cavity. Finally, the agent seeks the help of the baseline agent in order to decide whether the extracted cavity is above the baseline or not.
The basic algorithmic steps of this agent are illustrated in Algorithm 5. If the distance between every consecutive three end points is within a threshold, then the detected region is the letter “ﺳ”. The baseline agent is necessary to decide whether the extracted area is above the baseline or not. The Segmentation Phase The goal of this stage is to find the initial candidate cutting points that separate word’s characters correctly.
After agents detect regions that look like some of Arabic characters, these regions are subtracted from the whole word and the remaining parts are left for further processing, as shown in Figure 10. This step is important to locate potential segmentation places; the center of every two successive end points is a candidate segmentation point, see Figure 11.
Cutting points that segment the word more than once are eliminated from the candidate set.
As summarized in Table 2, 74% of the selected words consist of loops, 84% include under-baseline cavities with different shapes, and 54% contain above-baseline cavities with left and right directions. The segmentation results are classified into three classes: words that are correctly-segmented, words that are over-segmented, and words that are wrongly or under-segmented. Table 3" target="_self"> Table 3 displays the percentage of the identified segmentation points in each class, as follows.
The agents work well in detecting their related regions and hence the segmentation was done successfully.
The segmentation of Arabic word “Elfayedh”, written by four writers.
The segmentation of Arabic word “Nahhaal”, written by seven writers.
The segmentation of Arabic word “Elmanzah”, written by nine writers. The letter “ض” was considered as two letters, one consists of a loop and the other has a cavity.
This problem appears when a number of adjacent letters are extracted by the Seen agent and returned as letter seen because they have the same features as it. Conclusions We introduced a multi-agents approach to segment Arabic handwritten text. Our proposed methodology employs seven agents that are applied on the thinned image of the handwritten text; the agents cooperate to identify illegal segmentation points. Specific set of topologybased rules are used to the remaining parts of the thinned image to determine the set of potential cut-points that will lead to a successful segregation of individual characters. Our future work is to include more styles of Arabic handwritings and more agents to improve segmentation results. Kukarni, “Recent Achievements in Off-Line Handwriting Recognition Systems,” International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’98), Melbourne, 1998, pp.
Rigoll, “Confidence Measures for an Address Reading System,” Seventh International Conference on Document Analysis and Recognition (ICDAR2003), 2003, pp. Shridar, “Character Segmentation in Handwritten Words—An Overview,” Pattern Recognition, Vol.
Wakahara, “The State of The Art in On-line Handwriting Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. Bozinovic, “A Multi-Level Perception Approach to Reading Cursive Script,” Artificial Intelligence, Vol. Sabourin, “Segmentation of Arabic Cursive Script,” Proceedings of the Fourth International Conference on Document Analysis and Recognition, Ulm, 18-20 August 1997, Vol.
Verma, “An Artificial Neural Network Based Segmentation Algorithm for Off-Line Handwriting Recognition,” International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’98), Melbourne, 1997. Roy, “Character Segmentation for Handwritten Bangla Words Using Artificial Neural Network,” Proceeding of the First IAPR TC3 NNLDAR Workshop, Seoul, 2005.

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