Persian Text Based Traffic sign Detection with Convolutional Neural Network: A New Dataset

Kheirinejad, Saba; Riaihi, Noushin; Azmi, Reza

doi:10.1109/iccke50421.2020.9303646

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…As far as we know, few benchmark datasets for textbased traffic panel detection are publicly available [45,40]. Hence, this study gathered a novel public dataset all over the city of Tehran-Iran and called it 'Persian Text-Based Traffic Panel Dataset' [46]. As well as the general challenges mentioned above, there are other exclusive challenges for the Persian dataset: I) As shown in Figure 4, some traffic panels are not uniform from the perspective of color.…”

Section: Text-based Traffic Panelsmentioning

confidence: 99%

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

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Lately, traffic panel detection has been engrossed by academia and industry. This study proposes a new categorization method for traffic panels. The traffic panels are classified into three classes: symbol-based, text-based, and supplementary/additional traffic panels. Although few types of research have investigated text-based traffic panels, this type is considered in detail in this study. However, there are many challenges in this type of traffic panel, such as having different languages in different countries, their similarity with other text panels, and the lack of suitable quality datasets. The panels need to be detected first to obtain a reasonable accuracy in recognizing the text. Since there are few public text-based traffic panels datasets, this study gathered a novel dataset for the Persian textbased traffic panels all over the streets of Tehran-Iran. This dataset includes two collections of images. The first collection has 9294 images, and the latter has 3305 images. The latter dataset is more monotonous than the first one. Thus, the latter is utilized as the main dataset, and the first is used as an additional dataset. To this end, the algorithm uses the additional dataset for pre-training and the main datasets for training the network. The tiny YOLOv3 algorithm that is fast and has low complexity compared to the YOLOv3 is used for pre-training, training, and testing the data to examine the utility and advantages of the data. The K-fold cross-validation procedure is used to estimate the model's skill on the new data. It achieves 0.973 for Precision, 0.945 for Recall, and 0.955 for Fmeasure.

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Section: Text-based Traffic Panelsmentioning

confidence: 99%

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

Self Cite

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“…Considering text as object, the existing methodologies can be applied for text detection. This concept is mainly followed in the detection and recognition of text in different languages including English [46][47][48][49], Chinese [50][51][52], Arabic [53,54], Persian [55,56], Urdu [34,[57][58][59][60] and Hindi [61][62][63]. In some languages (including Urdu), there exists multiple ligatures and orthography.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Layer Holistic Approach for Cursive Text Recognition

et al. 2022

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Urdu is a widely spoken and narrated language in several South-Asian countries and communities worldwide. It is relatively hard to recognize Urdu text compared to other languages due to its cursive writing style. The Urdu text script belongs to a non-Latin cursive family script like Arabic, Hindi and Chinese. Urdu is written in several writing styles, among which `Nastaleeq’ is the most popular and widely used font style. A gap still poses a challenge for localization/detection and recognition of Urdu Nastaleeq text as it follows modified version of Arabic script. This research study presents a methodology to recognize and classify Urdu text in Nastaleeq font, regardless of the text position in the image. The proposed solution is comprised of a two-step methodology. In the first step, text detection is performed using the Connected Component Analysis (CCA) and Long Short-Term Memory Neural Network (LSTM). In the second step, a hybrid Convolution Neural Network and Recurrent Neural Network (CNN-RNN) architecture is deployed to recognize the detected text. The image containing Urdu text is binarized and segmented to produce a single-line text image fed to the hybrid CNN-RNN model, which recognizes the text and saves it in a text file. The proposed technique outperforms the existing ones by achieving an overall accuracy of 97.47%.

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