A Novel Arabic Baseline Estimation Algorithm Based on Sub-Words Treatment

Boukerma, Hanene; Farah, Nadir

doi:10.1109/icfhr.2010.58

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…The paper discusses seven main stages involved in this method; connected component allocation, candidate baseline regions word, baseline straightening procedure and lastly text slant correction for performance of the proposed method on the textual binary images. The results on the IFN/ENIT the experiments with the proposed method show a significantly ], Boukerma [8] methods. The proposed method will enhance the performances of several raction and character…”

Section: Experiments and Resultsmentioning

confidence: 89%

“…Many experiments are conducted on the IFN/ENIT dataset [20] to validate the abilities of the proposed framework. To evaluate the proposed method performance, the results of the proposed method are compared visually with the results of Pechwitz [6], Farooq [7], Boukerma [8] (see Figure 6). Based on Figure 6, the proposed algorithm method shows better results.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Set of experiments are conducted on a benchmark database for Arabic handwritten text called IFN/ENIT. The result is compared with a set of well-known methods such as Pechwitz [6], Farooq [7], Boukerma [8] and Horizontal Projection Histogram [9]- [11]. The results of the proposed method show a better performance than the previous methods.…”

Section: Arabic Handwriting Script; Baseline Detection; Handwritten Textmentioning

confidence: 96%

“…In another study, Boukerma proposed an algorithm method that uses a piece wise painting scheme to estimate the baseline by identifying a set of points used in the estimation process [8]. However, this method is defective in cases where large diacritics and small characters exist.…”

Section: Related Studiesmentioning

confidence: 99%

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Text Normalization Method for Arabic Handwritten Script

et al. 2013

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Text normalization is an important technique in document image analysis and recognition. It consists of many preprocessing stages, which include slope correction, text padding, skew correction, and straight the writ this side, text normalization has an important role in many procedures such as text segmentation, feature extraction and characters recognition. In the present article, a new method for text baseline detection, straightening, and slant correction for Arabic handwritten texts is proposed. The method comprises a set of sequential steps: first components segmentation is done followed by components text thinning; then, the direc extracted, and the candidate baseline regions are determined. After that, selection of the correct baseline region is done, and finally, the baselines of all components are aligned with the writing line. The experiments IFN/ENIT benchmark Arabic dataset. The results show that the proposed method has a promising and encouraging performance.

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Section: Experiments and Resultsmentioning

confidence: 89%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Arabic Handwriting Script; Baseline Detection; Handwritten Textmentioning

confidence: 96%

Section: Related Studiesmentioning

confidence: 99%

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Text Normalization Method for Arabic Handwritten Script

et al. 2013

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“…The detected curve was implemented to detect several candidate points before detecting the baseline. Boukerma and Farah [35] presented an algorithm for Arabic baseline detection in sub-word level based on the skeleton of the text and the horizontal segmentation. This method was accomplished in two stages; extract of feature points and the sub-word contour.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Algorithm for Baseline Detection of Offline Arabic Handwritten Text Recognition

Ahmad Mustafa Ali Al Masri,

Muhammad Suzuri Hitam,

Wan Nural Jawahir Hj Wan Yussof

et al. 2024

ARASET

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The baseline detection is one of the challenging tasks in the pre-processing stage of an online Arabic handwritten text recognition. The challenges include text skewness, touching letters or words, short words, sub-words, ligatures, isolated characters, small descenders, and diacritics. This paper presents a novel automated algorithm for baseline detection that is able to overcome the previously mentioned challenges. The proposed baseline algorithm mainly used a skeletonizing algorithm to estimate the baseline for each sub-word that appear in the image separately. The proposed algorithm had been tested using the benchmark handwritten Arabic database, i.e., IFN/ENIT database. The experimental results showed that it is able to reduce the average error between the actual baseline and the resulting baseline to 3.39 pixels for all sub-words and achieved an average of 3.6% in the ratio between the error pixels and the image height. In addition, the proposed algorithm shows that it is superior as compared to four others benchmarked baseline detection algorithms. It is anticipated that this algorithm will be able to be applied for many Arabic offline handwritten applications in the future.

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