An Efficient Skewed Line Segmentation Technique for Cursive Script OCR

Malik, Saud Ahmed; Sajid, Ahthasham; Ahmad, Arshad; Almogren, Ahmad; Hayat, Bashir; Awais, Muhammad; Kim, Kyong Hoon

doi:10.1155/2020/8866041

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…The handwritten script is usually written in cursive, even in Latin scripts. Segmenting cursive scripts is more complicated than segmenting printed scripts [31,32]. Handwritten character recognition has essential applications such as sorting postal mail, processing handwritten forms, and processing bank checks [33].…”

Section: Literature Review 21 Printed and Handwritten Ocrmentioning

confidence: 99%

“…Scanned images or images taken from a phone could suffer from the skew problem and positioning the image in the correct direction enhances the recognition rate of the OCR system. Malik et al [32] and Ahmad et al [40] proposed Arabic OCR that corrects the skewness in the input image reported that the skewness correction enhanced the character segmentation. In Fig.…”

Section: Image and Video-based Ocrmentioning

confidence: 99%

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Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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This study aims to review the latest contributions in Arabic Optical Character Recognition (OCR) during the last decade, which helps interested researchers know the existing techniques and extend or adapt them accordingly. The study describes the characteristics of the Arabic language, different types of OCR systems, different stages of the Arabic OCR system, the researcher's contributions in each step, and the evaluation metrics for OCR. The study reviews the existing datasets for the Arabic OCR and their characteristics. Additionally, this study implemented some preprocessing and segmentation stages of Arabic OCR. The study compares the performance of the existing methods in terms of recognition accuracy. In addition to researchers' OCR methods, commercial and open-source systems are used in the comparison. The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters. Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment, and few approaches were tested on pagelevel scripts. Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low, under 75% for printed text, and further improvement is needed. Moreover, most of the current approaches are offline OCR systems, and there is no remarkable contribution to online OCR systems.

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Section: Literature Review 21 Printed and Handwritten Ocrmentioning

confidence: 99%

Section: Image and Video-based Ocrmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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“…This dependency also affects the accuracy or Urdu OCR. The proposed Urdu OCR systems 13,28,29,46 which achieve good accuracy are trained on particular font sizes hence their performance decreases on a different font size on which the proposed systems are not trained. The absence of bench mark Urdu OCR corpora also remained a problem for so many years.…”

Section: Ta B L E 1 a Comparison Between The Existing Techniques Of O...mentioning

confidence: 99%

Meta‐feature based few‐shot Siamese learning for Urdu optical character recognition

Naseer

Zafar

2022

Computational Intelligence

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Standard convolution neural network (CNN) achieves high level of accuracy for the recognition of characters in different languages. However, like other deep neural networks, training of CNN requires a substantial amount of data. Lack of sufficient training data invokes dataset bias, during learning process, which leads to a decay in the performance of CNN. The limitation of training data can be addressed by using few-shot learners. In this research, CNN-based few-shot Siamese learner is trained on meta-features, extracted from Urdu text images using a novel graph-based normal to tangent line (GNTL) technique, for Urdu optical character recognition (OCR) across different font sizes. The learner is trained on three corpora (datasets) including one benchmark corpus "Centre for Language Engineering Text Images" and two other corpora, that is, "Urdu Thickness Graphs" (UTG) and "Urdu OCR Font 16 to 36" (UOF) which are developed and released in this research. 80% of data is used for training while 20% of data is used for testing. To create UTG corpus, the proposed novel feature extraction technique GNTL is used and a meta-features-based corpus is developed in form of thickness graphs. The third corpus UOF is based on five different font sizes, that is, 16, 20, 26, 30, and 36. The performance of few-shot Siamese learner is compared with a standard CNN, trained on the same three corpora. Meta-feature based few-shot Siamese learner achieves a promising recognition accuracy and outperforms

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“…ese articles on Manchu character recognition are all based on character segmentation and used manual design to extract shallow features. However, the segmentation-based OCR method is based on accurate character segmentation technology [21]. For historical document images, due to the complexity of various typefaces and styles, inconsistent lighting during image capture, noise caused by capture equipment, and variable background colors (among other idiosyncrasies), correct segmentation of characters becomes more challenging.…”

Section: Manchu Language Ocrmentioning

confidence: 99%

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

Zhang

Liu

Wang

et al. 2021

Scientific Programming

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Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.

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An Efficient Skewed Line Segmentation Technique for Cursive Script OCR

Cited by 9 publications

References 25 publications

Arabic Optical Character Recognition: A Review

Arabic Optical Character Recognition: A Review

Meta‐feature based few‐shot Siamese learning for Urdu optical character recognition

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

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