Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features

Abuzaraida, Mustafa Ali; Elmehrek, Mohammed; Elsomadi, Esam

doi:10.11591/ijece.v11i4.pp3584-3592

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…In general, the method performed well and yielded elevated accuracy and efficiency levels in the conducted experiments. [30] 2021 In this paper, a new segmentation technique has been created and developed…”

Section: Discussionmentioning

confidence: 99%

Segmentation techniques for Arabic handwritten: a review

Abdalla Sheikh,

Sanusi Azmi,

Abdel Karim Abuain

et al. 2024

IJECE

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Image segmentation refers to the process of partitioning a page into distinct sections. This technique aims to improve and transform the image's representation into a more coherent and user-friendly format. Its common application involves identifying objects and boundaries (such as lines and curves) within images. However, this paper focuses on discussing segmentation methods specifically tailored for Arabic handwritten content. Dealing with the segmentation of Arabic handwritten material poses a significant challenge due to the diverse handwriting styles and the interconnection between Arabic letters. The paper will also touch on the classification of segmentation algorithms originally designed for modern documents, illustrating their adaptation in document processing. Furthermore, the paper will address the difficulties associated with segmenting Arabic handwritten content, including variations in writing style, the connected nature of Arabic characters, the complexity of Arabic cursive writing and as well as the diacritics challenges. Lastly, a concise overview of previously widely used segmentation techniques in various research endeavors will be provided.

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Section: Discussionmentioning

confidence: 99%

Segmentation techniques for Arabic handwritten: a review

Abdalla Sheikh,

Sanusi Azmi,

Abdel Karim Abuain

et al. 2024

IJECE

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“…This research is focused on offline handwriting recognition. Another research has explored online HCR and PCR discussed in [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

Balinese Script Handwriting Recognition Using Faster R-CNN

Pratama,

Sulistiyo,

Ihsan

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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In Balinese culture, the ability to read Balinese script is one of the challenges young generations face. Advances in machine learning have proposed handwriting detection systems using both traditional and deep learning models. However, the traditional approach is usually impractical and is prone to inaccurate identification results. Convolutional Neural Network (CNN)-based models integrate feature extraction and classification into an end-to-end pipeline to increase performance. This research proposes that recognizing characters through an object detection approach makes an end-to-end process of localizing and classifying several characters simultaneously using the Faster R-CNN. Four CNN models, including ResNet-50, ResNet-101, ResNet-152, and Inception ResNet V2 were tested to detect 28 Balinese characters in a single form covering 18 consonants and 10 digits using Intersection over Union (IoU) thresholds: 0.5 and 0.75. ResNet-50 and Inception ResNet V2 achieve 0.991 mAP at IoU of 0.5, while Inception ResNet V2 excels at IoU of 0.75. Further analysis showed that class “nol” had the lowest Recall due to many undetected ground truths. Meanwhile, class “ba” had the lowest Precision due to its similarity with classes “ga” and “nga”. This research contributes to experimenting with Faster R-CNN in detecting handwritten Balinese scripts.

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“…Text-line, word, and character segmentation is the technique by which the fundamental elements in a text document image are localized and extracted. Segmentation is a critical stage for handwriting and printed recognition, it is the most important step in in online and offline character recognition [1], [2]. It is the most important and most challenging phase in optical character recognition (OCR).…”

Section: Introductionmentioning

confidence: 99%

An approach to analysis of arabic text documents into text lines, words, and characters

Abdo

Abdu

Manza

et al. 2022

IJEECS

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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 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 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.

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Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features

Cited by 10 publications

References 7 publications

Segmentation techniques for Arabic handwritten: a review

Segmentation techniques for Arabic handwritten: a review

Balinese Script Handwriting Recognition Using Faster R-CNN

An approach to analysis of arabic text documents into text lines, words, and characters

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