Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding

Do, Thao; Tolcha, Yalew; Jun, Tae Joon; Kim, Dae-Young

doi:10.1109/icpr48806.2021.9412707

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Especially a barcode recognition task, which is identified as a backbone of SCM, could achieve its goals efficiently and effectively when applying the DL method. DL could improve both qualities of barcode images with better clearness, as well as fineness [10][11][12], and barcode analysis performance with greater accuracy and real-time performance [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches

Kamnardsiri

Charoenkwan

Malang

et al. 2022

Sensors

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Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with ground truth data, which is often not readily available in the case of SCM barcode recognition. This study introduces two invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied and compared over a set of recently available barcode datasets. To confirm the model’s performance and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU thresholds and image transformation settings. The results show that YOLO v5 works best for the ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are available for developing barcode recognition models and positively affect comparative studies.

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Section: Introductionmentioning

confidence: 99%

1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches

Kamnardsiri

Charoenkwan

Malang

et al. 2022

Sensors

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Barcode Image Identification Based on Maximum a Posterior Probability

Kim

Kang²

2022

2022 IEEE International Conference on Consumer Electronics (ICCE)

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QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time

Do¹,

Kim²

2021

Preprint

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There are many real-life use cases such as barcode scanning or billboard reading where people need to detect objects and read the object contents. Commonly existing methods are first trying to localize object regions, then determine layout and lastly classify content units. However, for simple fixed structured objects like license plates, this approach becomes overkill and lengthy to run. This work aims to solve this detect-and-read problem in a lightweight way by integrating multi-digit recognition into a one-stage object detection model. Our unified method not only eliminates the duplication in feature extraction (one for localizing, one again for classifying) but also provides useful contextual information around object regions for classification. Additionally, our choice of backbones and modifications in architecture, loss function, data augmentation and training make the method robust, efficient and speedy. Secondly, we made a public benchmark dataset of diverse real-life 1D barcodes for a reliable evaluation, which we collected, annotated and checked carefully. Eventually, experimental results prove the method's efficiency on the barcode problem by outperforming industrial tools in both detecting and decoding rates with a real-time fps at a VGA-similar resolution. It also did a great job expectedly on the license-plate recognition task (on the AOLP dataset) by outperforming the current state-of-the-art method significantly in terms of recognition rate and inference time.

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Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding

Cited by 3 publications

References 19 publications

1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches

1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches

Barcode Image Identification Based on Maximum a Posterior Probability

QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time

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