Isolated Handwritten Pashto Character Recognition Using a <i>K</i>‐NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

Huang, Juanjuan; Haq, Ihtisham Ul; Dai, Chaolan; Khan, Sulaiman; Nazir, Shah; Imtiaz, Muhammad

doi:10.1155/2021/5558373

Cited by 24 publications

(11 citation statements)

References 29 publications

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“…Second, we see potential in using a transformer in an end-to-end text spotting system. Third, TRIG can be improved to solve the problem about other languages [54] and hand-written (cursive) text recognition [55,56].…”

Section: Discussionmentioning

confidence: 99%

TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance

Yue

Jia

et al. 2021

Electronics

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Scene text recognition (STR) is an important bridge between images and text, attracting abundant research attention. While convolutional neural networks (CNNS) have achieved remarkable progress in this task, most of the existing works need an extra module (context modeling module) to help CNN to capture global dependencies to solve the inductive bias and strengthen the relationship between text features. Recently, the transformer has been proposed as a promising network for global context modeling by self-attention mechanism, but one of the main short-comings, when applied to recognition, is the efficiency. We propose a 1-D split to address the challenges of complexity and replace the CNN with the transformer encoder to reduce the need for a context modeling module. Furthermore, recent methods use a frozen initial embedding to guide the decoder to decode the features to text, leading to a loss of accuracy. We propose to use a learnable initial embedding learned from the transformer encoder to make it adaptive to different input images. Above all, we introduce a novel architecture for text recognition, named TRansformer-based text recognizer with Initial embedding Guidance (TRIG), composed of three stages (transformation, feature extraction, and prediction). Extensive experiments show that our approach can achieve state-of-the-art on text recognition benchmarks.

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Section: Discussionmentioning

confidence: 99%

TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance

Yue

Jia

et al. 2021

Electronics

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“…They were used by many Arabic OCR systems [69][70][71]. Zoning is used to extract features by dividing the image into regions of equal sizes horizontally and vertically, and then the regions with the black pixels are used as a feature by calculating the sum of pixels intensities at each zone [72,73].…”

Section: Handcrafted Featuresmentioning

confidence: 99%

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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“…To overcome this problem, gamma is used to improve the illumination robustness of the tire image to reduce the influence of the illumination intensity change. Assuming the gamma value is 1/2, the gamma compression transformation formula is as follows [24]:…”

Section: Computational Gradients Of Pixelsmentioning

confidence: 99%

Tire appearance defect detection method via combining HOG and LBP features

Liu

Jia

et al. 2023

Front. Phys.

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Tire appearance defect detection based on machine vision is an effective technology to improve the tire production quality. The detection process can be completed by the way of non-destructive testing. Therefore, more and more researchers are paying attention to this technology. However, tires are characterized by single block colors and various defects. It is a great challenge to accurately detect tire appearance defects. To complete the task of detecting tire defects, this paper presents a novel tire appearance defect detection method via combining histogram of oriented gradients (HOG) and local binary pattern (LBP) features. First, we construct a tire image dataset to provide defective and normal tire images. Then, histogram of oriented gradients and local binary pattern features of tire images are, respectively, extracted and used to train the support vector machine (SVM) classifier. Finally, the support vector machine classifier calculates the prediction scores of the test images via combining the histogram of oriented gradients and local binary pattern features. These scores can be utilized to determine whether the test image is a defective or a normal tire image, and the goal of tire appearance defect detection is achieved. Conducted on the tire image dataset, our method has verified the effectiveness of detecting tire detects, and the mean accuracy is improved more than 1.6% than the algorithm that only uses the histogram of oriented gradients or local binary pattern feature. The experimental results demonstrate that the combination of HOG and LBP features can increase tire appearance defect detection accuracy.

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Isolated Handwritten Pashto Character Recognition Using a K‐NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

Cited by 24 publications

References 29 publications

TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance

TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance

Arabic Optical Character Recognition: A Review

Tire appearance defect detection method via combining HOG and LBP features

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