Recognition of Expiration Dates Written on Food Packages with Open Source OCR

Hosozawa, Kento; Wijaya, Ricky; Linh, Tran Duy; Seya, Hiroaki; Arai, Masayuki; Maekawa, Tsukasa; Mizutani, Kozo

doi:10.7763/ijcte.2018.v10.1220

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…To see the quality of the food one of the most important parameters is its expiration date. Mobile application was developed to be f printed expiration dates [16]. warn device was proposed for identification of the temporal similarity between different types of food.…”

Section: Materials and Methodology 31 Datasetmentioning

confidence: 99%

Deep Transfer Learning for Food Recognition

et al. 2019

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Section: Materials and Methodology 31 Datasetmentioning

confidence: 99%

Deep Transfer Learning for Food Recognition

et al. 2019

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“…Angle correction and erosion thresholding operations are shown in Figure 4. In Hosozawa et al [15], an application for use on smartphones is developed in order to identify expiration dates printed on industrialized food packaging. The authors propose that application users use their smartphone to capture a photo of the expiration date and the item in question before storing it in a refrigerator for perishables items.…”

Section: Concepts About Text Detection and Recognition And Similar Re...mentioning

confidence: 99%

“…Relation between problems encountered and proposed solutions. Adapted from [15] Problem Proposed solution Considering that the research proposal is the development of an Android application, items (1) and ( 2) are resolved directly in the application, where it is possible to rotate and crop the image as the user wishes. Problem (3) is solved with thresholding, and problems (4) and ( 5) with the dilation method, as shown in Figure 7.…”

Section: Concepts About Text Detection and Recognition And Similar Re...mentioning

confidence: 99%

“…The acceptance of the equivalence of some characters proved to be fundamental, especially the exchange of "V" for "v", as well as the angle correction, proposed by Zaafouri et al [14]. The dilation technique proposed by Hosozawa et al [15] was also very helpful to achieve acceptable results. The adaptive threshold combined with denoise proved to be a reasonable solution to the thresholding problem.…”

Section: Volume 2 Issue 2|2022| 61mentioning

confidence: 99%

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Test and Development of a Low-Cost Solution for Automatic Inspection of PET Bottle Codification

Souza

Fernandes

2022

Digital Manufacturing Technology

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Quality of packaging is a key factor in consumer choice for a particular product. Besides the package appearance, legal requirements demand that certain information is presented to the consumer. The main goal of this work is to evaluate the technical feasibility of using a Raspberry Pi board as a platform for automatic quality inspection for the expiration date and batch information in polyethylene terephthalate (PET) bottles. The system consists in capturing digital images using a Raspberry Pi Camera, processing the image obtained and performing optical character recognition in order to determine if the coding is compliant. Two models of bottles were used, and 24 images of each were captured, 15 of which were examples of compliant bottles and 9 were defective. The tests were divided into two sequences of processing methods. The best sequence achieved 100% specificity, 91.7% accuracy for model A bottle and 83.3% for model B.

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“…Their experimental results indicated that the accuracy of their method for recognizing dot matrix characters was only 43%. Hosozawa et al proposed an erosion and dilation procedure for dot matrix characters [ 3 ]. However, they did not give the recognition accuracy for this procedure.…”

Section: Introductionmentioning

confidence: 99%

Expiry-Date Recognition System Using Combination of Deep Neural Networks for Visually Impaired

Ashino

Takeuchi

2020

Lecture Notes in Computer Science

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Many drink packages have expiry dates written in dot matrix characters (digits and non-digits, e.g., slashes or dots). We collected images of these packages and trained two existing deep neural networks (DNNs) to combine and form a system for detecting and recognizing expiry dates on drink packages. One of the DNNs is an object-detection DNN and the other is a character-recognition DNN. The object-detection DNN alone can localize the characters written on a drink package but its recognition accuracy is not sufficient. The character-recognition DNN alone cannot localize characters but has good recognition accuracy. Because the system is a combination of these two DNNs, it improves the recognition accuracy. The object-detection DNN is first used to detect and recognize the expiry date by localizing and obtaining the size of the character. It then scans the expiry-date region and clips the image. The character-recognition DNN then recognizes the characters from the clipped images. Finally, the system uses both DNNs to obtain the most accurate recognition result based on the spacing of the digits. We conducted an experiment to recognize the expiry dates written on the drink package. The experimental results indicate that the recognition accuracy of the object-detection DNN alone was 90%, that of the character-recognition DNN alone was also 90%, and that combining the results of both DNNs was 97%.

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Recognition of Expiration Dates Written on Food Packages with Open Source OCR

Cited by 16 publications

References 9 publications

Deep Transfer Learning for Food Recognition

Deep Transfer Learning for Food Recognition

Test and Development of a Low-Cost Solution for Automatic Inspection of PET Bottle Codification

Expiry-Date Recognition System Using Combination of Deep Neural Networks for Visually Impaired

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