Abstract
With the continuous development of the social economy, the problem of low efficiency of invoice reimbursement has received more and more attention from companies, universities, and governments in China. In this paper, based on the recognition of invoices by OCR, we use Hough transform to preprocess the scanned image of invoices and creatively introduce the idea of checking the amount of money. We proofread the uppercase and lowercase amounts in the OCR recognition results. Using this method, the accuracy rate of OCR recognition increased from 95 to 99%, which greatly reduced the employees’ reimbursement time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yu F, You-guo PI. Research and implementation on common machine-printed commercial invoice recognition system. Inf Technol. 2013;37(6):36–40.
He W. Studying vehicle sales invoice recognition algorithm. Guangzhou: South China University of Technology; 2010.
Liu D, Chen Y. A courtesy amount recognition system for Chinese bank checks. In: 2012 international conference on frontiers in handwriting recognition; 2012. p. 710–5.
Gorski N, Anisimov V, Augustin E. A2iA check reader: a family of bank check recognition systems. In: Proceedings of the fifth international conference on Bangalore; 1999. p. 523–6.
Singh S, Kariveda T, Gupta JD. Handwritten words recognition for legal amounts of bank cheques in English script. In: 2015 eighth international conference on advances in pattern recognition (ICAPR); 2015. p. 1–5.
Sneha S, Kariveda T, Gupta JD, Bhattacharya K. Handwritten words recognition for legal amounts of bank cheques in English script. In: 2015 Eighth international conference on advances in pattern recognition (ICAPR); 2015. p. 1–5.
Liu D, Chen Y. A courtesy amount recognition system for Chinese bank checks. In: 2012 international conference on frontiers in handwriting recognition; 2010. p. 710–5.
Jayadevan R, Pal U, Kimura F. Recognition of words from legal amounts of Indian Bank Cheques. In: 2010 12th international conference on frontiers in handwriting recognition; 2010. p. 166–71.
Kaur R, Pooja. A non OCR approach for math CAPTCHA design based on Boolean algebra using digital gates to enhance web security. In: 2016 international conference on wireless communications, signal processing and networking (WiSPNET); 2016. p. 862–6.
Wankhede PA, Mohod SW. A different image content-based retrievals using OCR techniques. In: 2017 international conference of electronics, communication and aerospace technology (ICECA); 2017. p. 155–61.
Alghamdi MA, Alkhazi IS, Teahan WJ. Arabic OCR evaluation tool. In: 2016 7th international conference on computer science and information technology (CSIT); 2016. p. 1–6.
Ekram MAU, Chaudhary A, Yadav A, Khanal J, Asian S. Book organization checking algorithm using image segmentation and OCR. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS); 2017. p. 196–9.
Ganai AF, Lone FR. Character segmentation for Nastaleeq URDU OCR: a review. In: 2016 international conference on electrical, electronics, and optimization techniques (ICEEOT); 2016, p. 1489–93.
Rawls S, Cao H, Sabir E, Natarajan P. Combining deep learning and language modeling for segmentation-free OCR from raw pixels. In: 2017 1st international workshop on Arabic script analysis and recognition (ASAR); 2017. p. 119–23.
Jain V, Dubey A, Gupta A, Sharma S. Comparative analysis of machine learning algorithms in OCR. In: 2016 3rd international conference on computing for sustainable global development (INDIACom); 2016. p. 1089–92.
Xu B, Li R, Liu Y, Yan H, Li S, Zhang H. Filtering Chinese image spam using pseudo-OCR. Chin J Electron. 2015;24(1):134–9.
Budig B, van Dijk TC, Kirchner F. Glyph miner: a system for efficiently extracting glyphs from early prints in the context of OCR. In: 2016 IEEE/ACM joint conference on digital libraries (JCDL); 2016. p. 31–4.
Chiron G, Doucet A, Coustaty M, Visani M, Moreux JP. Impact of OCR errors on the use of digital libraries: towards a better access to information. In: 2017 ACM/IEEE joint conference on digital libraries (JCDL); 2017. p. 1–4.
Kakani BV, Gandhi D, Jani S. Improved OCR based automatic vehicle number plate recognition using features trained neural network. In: 2017 8th international conference on computing, communication and networking technologies (ICCCNT); 2017. p. 1–6.
Pranali B, Anil W, Kokhale S. Inhalt based video recuperation system using OCR and ASR technologies. In: 2015 international conference on computational intelligence and communication networks (CICN); 2015. p. 382–6.
Xu S, Smith D. Retrieving and combining repeated passages to improve OCR. In: 2017 ACM/IEEE joint conference on digital libraries (JCDL); 2017. p. 1–4.
Omran SS, Jarallah JA. Iraqi car license plate recognition using OCR. In: 2017 annual conference on new trends in information & communications technology applications (NTICT); 2017. p. 298–303.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yin, Y., Wang, Y., Jiang, Y., Fan, S., Xiong, J., Gui, G. (2020). The Image Preprocessing and Check of Amount for VAT Invoices. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-6504-1_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6503-4
Online ISBN: 978-981-13-6504-1
eBook Packages: EngineeringEngineering (R0)