Universal Barcode Detector via Semantic Segmentation

Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches

QR Codes Usage Approach In The Virtualized Consumption

Placed in magazines, newspapers, billboard, subway stations, airports, public places, advertising panels, public or private institutions, QR codes meet an increased popularity by instantly connecting any consumer to details of products, discounts, events, payment and purchasing services or direct access to any web address. All of these aspects already exist in any consumer’s life but in an unstructured process which now can be summarized by a single code scan, using a common camera based device. In this paper we determine whether the massive implementation of QR codes would accelerate virtualized consumption and perform towards profitability as a new strategic resource

QR Codes Usage Approach In The Virtualized Consumption

Placed in magazines, newspapers, billboard, subway stations, airports, public places, advertising panels, public or private institutions, QR codes meet an increased popularity by instantly connecting any consumer to details of products, discounts, events, payment and purchasing services or direct access to any web address. All of these aspects already exist in any consumer’s life but in an unstructured process which now can be summarized by a single code scan, using a common camera based device. In this paper we determine whether the massive implementation of QR codes would accelerate virtualized consumption and perform towards profitability as a new strategic resource

An Efficient Bar Code Image Recognition Algorithm for Sorting System

Copyright © The Authors. In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images. It results in bar code recognition rate being low and real time being poor. Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed. First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image. Failure indicates that the compression ratio is improper or the image is skewed. Then, the source image is enhanced to identify the source image directly. Finally, the inclination of the compressed image is detected by the barcode region recognition method and the source image is corrected to locate the barcode information in the barcode region recognition image. The results of multitype image experiments show that the proposed method is improved by 5+ times computational efficiency compared with the former methods, and can recognize fuzzy images better.Scientific Research Starting Project of SWPU [Zheng, D., No. 0202002131604]; Major Science and Technology Project of Sichuan Province [Zheng, D., No. 8ZDZX0143]; Ministry of Education Collaborative Education Project of China [Zheng, D., No. 952]; Fundamental Research Project [Zheng, D., Nos. 549, 550]

Identifikasi Barcode pada Gambar yang Ditangkap Kamera Digital Menggunakan Metode JST

AbstrakDewasa ini hampir setiap produk konsumen memiliki label barcode. Namun alat pembaca barcode jenis laser memiliki kelemahan karena tidak dapat mengenali barcode yang mengalami goresan atau noise. Namun telah dikembangkan teknik lain dengan memanfaatkan kamera digital untuk identifikasi barcode. JST telah banyak digunakan untuk identifikasi berbagai macam pola. Proses identifikasi barcode dalam JST terdiri dari proses training dan proses identifikasi. Proses training menggunakan metode LVQ (Learning Vector Quantization). Proses identifikasi terdiri dari beberapa tahap, yaitu akuisisi citra, preprocessing, locating barcode, proses pengujian dan verifikasi. Berdasarkan hasil pengujian metode LVQ dapat digunakan untuk identifikasi foto barcode dengan kinerja yang baik. Hasil pengujian menunjukkan tingkat akurasi sebesar 73,6 % dari 72 citra yang diuji dengan waktu rata-rata adalah 0.5 detik. Sementara waktu yang dibutuhkan untuk menemukan lokasi barcode adalah sekitar 6 detik menggunakan blok dengan ukuran 32x32 pixel. Kata kunci— Barcode, Learning Vector Quantization, Jaringan Syaraf Tiruan AbstrakIn today’s modern society, almost every consumer product has a barcode label. But the barcode reader with laser type has the disadvantage of not being able to recognize the barcode has a scratch or noise. However, other techniques have been developed by using a digital camera for barcode identification. ANN has been widely used for identification of various patterns. Barcode identification process consists of the ANN training process and the identification process. Training process using the LVQ (Learning Vector Quantization). Identification process consists of several stages: image acquisition, preprocessing, locating barcode, testing and verification process. Based on test results LVQ method can be used for photo identification barcode with good performance. The test results showed an accuracy of 73.6% rate of 72 images were tested with an average time is 0.5 seconds. While the time required to find the location of the barcode is about 6 seconds using a block size of 32x32 pixels. Keyword— Barcode, Learning Vector Quantization, Artificial Neural Networ

Pattern Recognition Using K-Nearest Neighbors (KNN) Technique

The aim of this project is to develop a better image processing algorithm using K-Nearest Neighbors (KNN) technique. This project will apply supervised learning, thus require the author to gather training data and testing data consist of written alphabets

Vision Based Extraction of Nutrition Information from Skewed Nutrition Labels

An important component of a healthy diet is the comprehension and retention of nutritional information and understanding of how different food items and nutritional constituents affect our bodies. In the U.S. and many other countries, nutritional information is primarily conveyed to consumers through nutrition labels (NLs) which can be found in all packaged food products. However, sometimes it becomes really challenging to utilize all this information available in these NLs even for consumers who are health conscious as they might not be familiar with nutritional terms or find it difficult to integrate nutritional data collection into their daily activities due to lack of time, motivation, or training. So it is essential to automate this data collection and interpretation process by integrating Computer Vision based algorithms to extract nutritional information from NLs because it improves the user’s ability to engage in continuous nutritional data collection and analysis. To make nutritional data collection more manageable and enjoyable for the users, we present a Proactive NUTrition Management System (PNUTS). PNUTS seeks to shift current research and clinical practices in nutrition management toward persuasion, automated nutritional information processing, and context-sensitive nutrition decision support. PNUTS consists of two modules, firstly a barcode scanning module which runs on smart phones and is capable of vision-based localization of One Dimensional (1D) Universal Product Code (UPC) and International Article Number (EAN) barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The algorithm localizes barcodes in images by computing Dominant Orientations of Gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The algorithm is implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. The algorithm was evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. The DOG algorithm was coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scan lines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass. This module is also coupled to a comprehensive NL database from which nutritional information can be retrieved on demand. Currently our NL database consists of more than 230,000 products. The second module of PNUTS is an algorithm whose objective is to determine the text skew angle of an NL image without constraining the angle’s magnitude. The horizontal, vertical, and diagonal matrices of the (Two Dimensional) 2D Haar Wavelet Transform are used to identify 2D points with significant intensity changes. The set of points is bounded with a minimum area rectangle whose rotation angle is the text’s skew. The algorithm’s performance is compared with the performance of five text skew detection algorithms on 1001 U.S. nutrition label images and 2200 single- and multi-column document images in multiple languages. To ensure the reproducibility of the reported results, the source code of the algorithm and the image data have been made publicly available. If the skew angle is estimated correctly, optical character recognition (OCR) techniques can be used to extract nutrition information