Recognition of Lottery Digits Using OCR Technology

Tangwannawit, Sakchai; Saetang, Wanida

doi:10.1109/sitis.2016.105

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…However, the Google Cloud Vision API may be less customizable owing to the availability of in-built services. When comparing the accuracy of the technique with other articles on Thai language recognition as detailed in Table 3, this paper found that the articles [33], [41]- [43] used the actual cases as a sample and there has higher accuracy than the proposed method. However, when studying the details of each article, article [41] recognized only six-digit numbers of the Thai lottery ticket.…”

Section: Discussion and Suggestionmentioning

confidence: 98%

“…When comparing the accuracy of the technique with other articles on Thai language recognition as detailed in Table 3, this paper found that the articles [33], [41]- [43] used the actual cases as a sample and there has higher accuracy than the proposed method. However, when studying the details of each article, article [41] recognized only six-digit numbers of the Thai lottery ticket. Articles [33], [42], [43] recognized the Thai sign and the car license plate.…”

Section: Discussion and Suggestionmentioning

confidence: 98%

“…In Thailand, computer vision has been used for more than ten years for scenarios such as citizen ID cards [30], translating Thai menus into Malay [31], and license plates [32]. Also, Thai character recognition is studied in various techniques, as shown in Table 3 97.14% Tangwannawit and Saetang [41] Method: The Tesseract OCR engine in a mobile application. Data: 50 tickets.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Thammarak

Kongkla

Sirisathitkul

et al. 2022

IJECE

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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.

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