An Efficient Segmentation Technique for Urdu Optical Character Recognizer (OCR)

Malik, Saud Ahmed; Maqsood, Muazzam; Aadil, Farhan; Khan, Muhammad Fahad

doi:10.1007/978-3-030-12385-7_11

Cited by 16 publications

(11 citation statements)

References 19 publications

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“…In a very recent work [56], the authors presented a simple and robust line segmentation algorithm for Urdu handwritten and printed text. In the proposed line segmentation algorithm, a modified header and a baseline detection method were used.…”

Section: Urdu Numeral Recognitionmentioning

confidence: 99%

Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

et al. 2019

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In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.

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Section: Urdu Numeral Recognitionmentioning

confidence: 99%

Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

et al. 2019

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“…A normal ICR framework involved of three stages: recognition, segmentation and preprocessing. In the stage of segmentation, the scanned images have been segmented into three levels displayed in Malik et al [53]. First level, the images of text have been segmented in order to extracting the baseline through utilizing a profile along with axis of Y, referred to as vertical projection.…”

Section: Arabic and Derived Languagesmentioning

confidence: 99%

Survey on Segmentation and Recognition of Handwritten Arabic Script

Ali

Suresha

2020

SN COMPUT. SCI.

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The issue of handwritten recognition in Arabic script nature has attracted many researchers from both academic and industrial fields. But their efforts have not reached satisfying outcomes till now. In this paper, a survey has been done in segmentation and recognition of handwritten documents in Arabic script. Most of the previous published works have been analyzed, and some remedies have been suggested. Various strategies used for creating a powerful recognition system have been summarized. This paper presents various algorithms with respect to text, word and characters segmentation and recognition of Arabic document. It analyzes the recognition stage of Arabic script depending on segmentation strategies.

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“… A character may have different shapes depending on where it appears in a word. For example, character /H/ ‫"ه"‬ is shown as /Guidance/ ‫"هدایت"‬ in the first letter of the word, as /Moonlight/ ‫"مهتاب"‬ in the middle of the word, and as /Blossom/ ‫"شکوفه"‬ at the end of the word, which also can be connected to other characters or not, such as /Simple/ ‫"ساده"‬ in which the character /H/ ‫"ه"‬ is not connected to its previous character [ 13].…”

Section: Introductionmentioning

confidence: 99%

A Rule-Based Post-Processing Approach to Improve Persian OCR Performance

et al. 2020

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Optical Character Recognition (OCR) is a system to convert images including text into an editable text. Nowadays, the accuracy of these systems in images with simple-structure and high quality is high. However, the performance degrades for images with complex-structure, low quality, and in the presence of noise, scratches, pictures, stamps, or other non-textual symbols. This paper proposes a Persian OCR post-processing technique to increase the accuracy of the OCR systems dealing with real-world challenging samples. The proposed method extracts five features in each line of the text and uses seven proposed rules to investigate whether that line should be ignored or not. To evaluate the proposed method, Khana (structural based) and Bina (deep learning-based) Persian OCR systems have utilized, and a dataset containing 200 complex-structure images has been collected and a dataset including 100 simple-structure images have been used. The accuracy of the Khana and Bina in images with complex-structure is 39% and 58%, respectively, while after applying the proposed post-processing method the accuracy increases to 93% and 91%, respectively.

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An Efficient Segmentation Technique for Urdu Optical Character Recognizer (OCR)

Cited by 16 publications

References 19 publications

Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

Survey on Segmentation and Recognition of Handwritten Arabic Script

A Rule-Based Post-Processing Approach to Improve Persian OCR Performance

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