A NOVEL APPROACH FOR SHIPPING CONTAINER CODE RECOGNITION

Lê Hoàng Thanh

Abstract


Optical character recognition is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded texts, whether from a scanned document, a photo of a document, a scene-photo or from subtitle text superimposed on an image. It is widely used as a form of information entry from printed data records including, passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation. Currently, in logistic, container code recognition is mainly done manually, so it is necessary to have a solution for automatic identification to save time and effort. This paper proposes a novel model for code recognition which can be applied for shipping containers widely used in logistics. The obtained experimental results have proved that the proposed model produces satisfactory confidence on a benchmark dataset.

Keywords


Character recognition; Container number; HOG; SVM.

References


Alpaydin, E. (2014). Introduction to machine learning.Massachusetts, USA: MIT Press.

Cha, S. H. (2001). Use of distance measures in handwriting analysis. (Doctoral Thesis), State University, New York. Retrieved from https://www.researchgate.net/ profile/Sung- Hyuk_Cha/publication/33787096 Use of_distance measures in handwriting analysis electronic_resource/links/00b495177e4a98d3b7000000.pdf

Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. Paper presented at The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), USA.

De Campos, T. E., Babu, B. R., & Varma, M. (2009). Character recognition in natural images. Paper presented at VISAPP 2009, Portugal.

Du, K. L., & Swamy, M. N. (2006). Neural networks in a soft computing framework. Berlin, Germany: Springer Science & Business Media.

Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. Paper presented at Machine Learning: The Thirteenth International Conference, Italy.

International Organization for Standardization – ISO. (1995).ISO 6346. Retrieved from https://www.iso.org/standard/20453.html

Lương, M. B. (2003). Nhập môn xử lý ảnh số. Hà Nội, Việt Nam: NXB Khoa học và Kỹ thuật.

Said, H. E., Tan, T. N., & Baker, K. D. (2000). Personal identification based on handwriting. Pattern Recognition, 33(1), 149-160.

Trần, H. C. (2013). Nhận dạng ký tự quang học. Hà Nội, Việt Nam: Trường Đại học Công Nghiệp Hà Nội.

Trier, Ø. D., Jain, A. K., & Taxt, T. (1996). Feature extraction methods for character recognition-a survey. Pattern Recognition, 29(4), 641-662.




DOI: http://dx.doi.org/10.37569/DalatUniversity.7.2.236(2017)

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