A study on handwritten parcel delivery invoice understanding model
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Optical character recognition (OCR) technology is a field of continuous research in which text images are stored or utilized as data. However, OCR technology alone has limitations in classifying and recognizing attribute values such as address, name, and phone number expressed in text in a semi-structured form, such as a parcel delivery invoice (PDI) written by hand. Therefore, in this study, we propose a handwritten parcel delivery invoice understanding (HPDIU) model for automated parcel delivery reception. The proposed HPDIU model consists of two steps: region detection of the parcel delivery invoice (RD-PDI) and information extraction from the PDI (IE-PDI). The RD-PDI, which is the first step, minimizes the resolution adjustment by detecting only the necessary area, including the sender and recipient information in the image. The second step, IE-PDI, consists of an end-to-end framework without OCR technology using a document understanding transformer and integrates the process of character detection, recognition, and understanding. In other words, the proposed model can solve the limitations of the OCR technology because it can integrate the process of classifying and recognizing according to attributes. To prove the validity of the proposed model, we used 500 handwritten PDI datasets to evaluate the accuracy of the character units according to the attributes of address, name, and phone number. As a result of the evaluation, a total average of 91.67% in units of letters proved the superiority of the proposed HPDIU model.
Abstract
Optical character recognition (OCR) technology is a field of continuous research in which text images are stored or utilized as data. However, OCR technology alone has limitations in classifying and recognizing attribute values such as address, name, and phone number expressed in text in a semi-structured form, such as a parcel delivery invoice (PDI) written by hand. Therefore, in this study, we propose a handwritten parcel delivery invoice understanding (HPDIU) model for automated parcel delivery reception. The proposed HPDIU model consists of two steps: region detection of the parcel delivery invoice (RD-PDI) and information extraction from the PDI (IE-PDI). The RD-PDI, which is the first step, minimizes the resolution adjustment by detecting only the necessary area, including the sender and recipient information in the image. The second step, IE-PDI, consists of an end-to-end framework without OCR technology using a document understanding transformer and integrates the process of character detection, recognition, and understanding. In other words, the proposed model can solve the limitations of the OCR technology because it can integrate the process of classifying and recognizing according to attributes. To prove the validity of the proposed model, we used 500 handwritten PDI datasets to evaluate the accuracy of the character units according to the attributes of address, name, and phone number. As a result of the evaluation, a total average of 91.67% in units of letters proved the superiority of the proposed HPDIU model.
참고문헌 (24)
- D. Wang, "Rout-ing and scheduling for hybrid truck-drone collaborative par-cel delivery with independent and truck-carried drones", IEEE Internet of Things Journal, 6(6), pp.10483~10495 (2019)
- E. Tallyn, "Enacting the last mile: Experiences of smart con-tracts in courier deliveries", Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp.1~14 (2021)
- T. T. H. Nguyen, "Survey of post-OCR processing approaches", ACM Com-puting Surveys (CSUR), 54(6), pp.1~37 (2022)
- N. Islam, "A survey on optical char-acter recognition system", arXiv preprint arXiv : 1710. 05703, ~ (2017)
- L. Kang, "Con-volutional neural networks for document image classifica-tion", 2014 22nd International Conference on Pattern Recognition, pp.3168~3172 (2014)
- M. Z. Afzal, "Deepdocclassifier: Document classification with deep convolutional neural network", 13th International Conference on Document Analysis and Recognition (ICDAR), pp.1111~1115 (2015)
- W. Hwang, "Post-OCR parsing: Building simple and robust parser via bio tagging", Workshop on Document Intelligence at NeurIPS (2019)
- B. P. Majumder, "Representation learning for information extrac-tion from form-like documents", Proceedings of the 58th Annual Meeting of the Association for Computational Lin-guistics, pp.6495~6504 (2020)
- M. Mathew, "DocVQA: A dataset for VQA on document images", Proceedings of the IEEE, 2021, 2199~2208
- R. Tito, "ICDAR 2021 competition on document visual question answering", International Conference on Docu-ment Analysis and Recognition, pp.635~649 (2021)
- R. Schaefer, "A two-step approach for automatic OCR postcorrection", Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, pp.52~57 (2020)
- S. Rijhwani, "OCR post correction for endangered language texts", Proceedings of the 2020 Conference on Empirical Methods in Natural Lan-guage Processing (EMNLP), pp.5931~5942 (2020)
- Q. Duong, "An unsuper-vised method for OCR post-correction and spelling normal-isation for Finnish", Proceedings of the 23rd Nordic Con-ference on Computational Linguistics (NoDaLiDa), pp.240~248 (2020)
- G. Kim, "OCR-free document under-standing transformer", European Conference on Computer Vision, pp.498~517 (2022)
- B. Shi, "An end-to-end trainable neural network for image-based sequence recognition and its ap-plication to scene text recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), pp.2298~2304 (2017)
- B. Shi, "Robust scene text recognition with automatic rectification", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4168~4176 (2016)
- W. Hwang, "Findings of the Association for Computational Linguistics", pp.330~343 (2021)
- W. Hwang, "Cost-ef-fective end-to-end information extraction for semi-struc-tured document images", Proceedings of the 2021 Confer-ence on Empirical Methods in Natural Language Pro-cessing, pp.3375~3383 (2021)
- A. Buades, "Non-local means de-noising", Image Processing On Line, 1, pp.208~212 (2011)
- Z. Liu, "Swin transformer: Hierarchical vision transformer using shifted windows", Proceedings of the IEEE, 2021, 9992~10002
- M. Lewis, "Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension", pp.7871~7880 (2020)
- J. Devlin, "Bert: Pre-training of deep bidirectional transformers for language un-derstanding", arXiv preprint arXiv:1810.04805, ~ (2018)
- A. Radford, "Improving language understanding by generative pre-training" (2018)
- D. Lewis, "Building a test collection for complex docu-ment information processing", Proceedings of the 29th An-nual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.665~666 (2006)