Automatic Detection and Recognition of Text-Based Traffic Signs from images

Oliveira, Gustavo Henrique de; Silva, Francisco Assis da; Pereira, Danillo Roberto; Almeida, Leandro Luiz de; Artero, Almir Olivette; Bonora, Alex Fernando; Albuquerque, Victor Hugo C. de

doi:10.1109/tla.2018.8804261

Cited by 8 publications

(5 citation statements)

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“…They labeled the connected components and considered all the blob candidates in a selection process and removed some of them based on their aspect ratio and size. Xavier et al [32] found edges of the input image using a canny edge detector. They used Hough transform to find the straight lines of the detected edges.…”

Section: The Methods That Extract the Features In A Traditional Waymentioning

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: The Methods That Extract the Features In A Traditional Waymentioning

confidence: 99%

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Kheirinejad¹,

Riahi²,

Azmi³

2022

J. Engg. Res. & Sci.

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“…However, in actual, both problems are addressed independently by researchers. Traffic signs containing symbol have been researched extensively [4][5][6][7][8]25], while text based traffic signs have received relatively less attention, regardless of the information they deliver [10][11][12][13][14]. The factors that have made symbol-based traffic sign detection a challenging task are even more of a broad challenge for the text in road signs.…”

Section: A Traffic Sign Detection and Recognition (Tsdr)mentioning

confidence: 99%

“…Nevertheless, detecting traffic signs containing symbol is frequently the foremost concern in the research field [4][5][6][7][8][9] such as circular signs detection, speed limit sign detection, stop sign detection and so on. Few studies have been conducted on the detection of traffic signs with text or text-based traffic panels [10][11][12][13][14][15] and one of the major challenge among others is the diversity of text in terms of font, scale, rotation, orientation, blur, dirt, haze and illumination which affects accuracy and time cost [13,16]. Furthermore, differences in language scripts among regions have contributed greatly in scarcity of algorithms for detecting text based road panels and traffic signs [17].…”

Section: Introductionmentioning

confidence: 99%

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

Khalid,

Shah,

Sharif

et al. 2024

IEEE Access

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Traffic signs have great importance regarding smooth traffic flow and safe driving. However, due to many distractions and capricious factors, spotting and perceiving them may become hazardous. Traffic sign detection and recognition have gained popularity to put an end or to lessen the issue, and massive efforts have been realized in this regard. Despite considerable endeavors put together for traffic sign detection and recognition, there is a lack of attention in this area where these traffic signs contain text in them. A handful of studies may be found in state-of-the-art (SOTA) methods for text-based traffic sign detection, and particularly lesser for text recognition of detected text. The proposed method focuses on developing a robust semi-pipeline intelligent system to detect and understand text from traffic road signs boards in various weather conditions. For this purpose, a customized YOLOv5s is deployed for initial panel detection. Subsequently, MSER with preprocessing techniques is used for localization of text. Finally, OCR with NLP is utilized to recognize the text. The proposed method employed the ASAYAR dataset for training and different datasets for testing. The proposed approach produced satisfactory outcomes on them in contrast with SOTA approaches.

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“…Agrawal e Kaur (2018) buscaram a extração de textos de imagens usando o algoritmo de Otsu (OTSU, 1979) para segmentação e o método de transformada de Hough para detecção de distorção. Oliveira et al (2018) buscaram aplicar diversas técnicas para conseguir identificar textos em placas informativas de trânsito, como usar intervalos de cores para encontrar a placa na imagem, identificação de contornos e segmentação para eliminar partes não importantes. Também existe a atividade de reconhecimento dos caracteres para se ter a busca das palavras desejadas.…”

Section: Introductionunclassified

“…Embora os algoritmos citados sejam de grande eficiência, os problemas demonstrados por Kuhn, Cervi e Manica (2018), Liang, Doermann e Li (2005), Miranda et al (2013), e por Oliveira et al (2018) podem atrapalhar muito os resultados, dificultando a identificação dos caracteres, e diminuindo a eficiência do algoritmo.…”

Section: Introductionunclassified

Busca De Palavras Chave Em Imagens De Livros Impressos Usando Visão Computacional

et al. 2021

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With the increasingly frequent use of books in digital format, people search for the desired subjects in a faster way compared to the search in physical books. This work aimed to develop a computational resource in the form of an application for Android smartphones, which, based on an image captured from a page in a book, performs searches by keywords. The purpose of using the application is to help the reader to find the desired information quickly. We use Computer Vision techniques with the aid of the OpenCV library in the development of algorithms to perform segmentation, correction of the perspective of the book page image, identification and rectification of the wavy lines, recognition and character classification. The results shown were promising with a hit rate of over 88%.

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Automatic Detection and Recognition of Text-Based Traffic Signs from images

Cited by 8 publications

References 0 publications

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

Busca De Palavras Chave Em Imagens De Livros Impressos Usando Visão Computacional

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