Rough Terrain Autonomous Vehicle Control Using Google Cloud Vision API

Sugadev, M.; Yogesh, .; Sanghamreddy, Prithvi Kumar; Samineni, Sai Krishna

doi:10.1109/icpedc47771.2019.9036621

Cited by 5 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Patel and Patel applied the Tesseract OCR and Transym OCR tools to consider vehicle number plates [23]. In another case related to an autonomous vehicle, Sugadev et al [11] applied the Google Cloud Vision API to process images, improve the obstacle detection accuracy, and provide the capability to identify obstacles for rough terrain autonomous robots. Moreover, many domains use this technology to increase the data entry speed and reduce human error in extracting data from document processing systems [24], [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Thammarak

Kongkla

Sirisathitkul

et al. 2022

IJECE

View full text Add to dashboard Cite

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Thammarak

Kongkla

Sirisathitkul

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…Many studies have focused on applying OCR or machine learning technology, and through modern research, an automated system with commercial cloud services, such as Tesseract, Google Cloud Vision, and Huawei Cloud OCR, has been developed. Sugadev et al [11] applied Google Cloud Vision to identify obstacles for rough-terrain autonomous robots. Putra et al [6] presented a web and mobile application for people with limited knowledge of medical images or no medical expertise.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several automatic tools have been developed to support an organization's needs in various areas, such as civil [4], environmental [5], medicine [6,7], travel [8], and finance [9,10]. Additionally, Google Cloud Vision is potentially one of the most widely used cloud services and is presently used in applications such as autonomous vehicles [11], visually impaired applications [12], and content-based image retrieval [13].…”

Section: Introductionmentioning

confidence: 99%

Automated Data Digitization System for Vehicle Registration Certificates Using Google Cloud Vision API

Thammarak

Sirisathitkul²,

Kongkla³

et al. 2022

Civ Eng J

View full text Add to dashboard Cite

This study aims to develop an automated data digitization system for the Thai vehicle registration certificate. It is the first system developed as a web service Application Programming Interface (API), which is essential for any enterprise to increase its business value. Currently, this system is available on “www.carjaidee.com”. The system involves four steps: 1) an embedded frame aligns a document to be correctly recognised in the image acquisition step; 2) sharpening and brightness filtering techniques to enhance image quality are applied in the pre-processing step; 3) the Google Cloud Vision API receives a prompt to proceed in the recognition step; 4) a specific domain dictionary to improve accuracy rate is developed for the post-processing step. This study defines 92 images for the experiment by counting the correct words and terms from the output. The findings suggest that the proposed method, which had an average accuracy of 93.28%, was significantly more accurate than the original method using only the Google Cloud Vision API. However, the system is limited because the dictionaries cannot automatically recognise a new word. In the future, we will explore solutions to this problem using natural language processing techniques. Doi: 10.28991/CEJ-2022-08-07-09 Full Text: PDF

show abstract

“…The paper [18] used the GCV API to analyse images in another scenario involving an autonomous vehicle to increase the accuracy of object identification and give toughenvironment autonomous robots the capacity to recognise objects. Additionally, many industries employ this system to speed up data entry and decrease human error when removing information from document management systems [19], [20].…”

Section: Literature Surveymentioning

confidence: 99%

“…OCR is a programme that converts text into an appropriate machine-readable format [18,19]. OCR technology is often used in businesses for automation and processing of written receipts [20].…”

Section: Steps Involved In Ocrmentioning

confidence: 99%

Machine Learning in OCR Technology: Performance Analysis of Different OCR Methods for Slide-to-Text Conversion in Lecture Videos

Hukkeri¹,

Goudar²,

Janagond³

et al. 2022

IJACSA

View full text Add to dashboard Cite

A significant percentage of a lecture video's content shown is text. Video text can therefore be a crucial source for automated video indexing. Researchers have recognised printed and handwritten text extracted from pictures using a variety of machine learning techniques and tools before digitising it. A machine learning technology called optical character recognition (OCR) enables us to recognise and retrieve text information from documents, converting it into searchable and editable data. This study primarily focuses on text extraction from lecture slides using Google Cloud Vision (GCV), Tesseract, Abbyy Finereader, and Transym OCR and compares the results to develop a lecture video indexing scheme for the non-linear steering in lecture videos to watch only the interesting points of topics. We have taken a total of 438 key-frames in 10 categories from seven different lecture videos that range in length. First, binary and greyscale versions of the input colour images are created. Before using the OCR APIs, the frames are additionally preprocessed to improve the image quality. The recognition accuracy demonstrated that the GCV OCR performs effectively, saving computing time by collecting image text with the highest accuracy of other tools, 96.7 percent.

show abstract

Rough Terrain Autonomous Vehicle Control Using Google Cloud Vision API

Cited by 5 publications

References 4 publications

Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Automated Data Digitization System for Vehicle Registration Certificates Using Google Cloud Vision API

Machine Learning in OCR Technology: Performance Analysis of Different OCR Methods for Slide-to-Text Conversion in Lecture Videos

Contact Info

Product

Resources

About