Recognition of OCR Invoice Metadata Block Types

Ha, Hien Thi; Medveď, Marek; Nevěřilová, Zuzana; Horák, Aleš

doi:10.1007/978-3-030-00794-2_33

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…This work mainly uses words, the smallest unit in document layout, to extract features. [37] OCRMiner system designed to extract the indexing metadata of structured documents obtained from an image scanning process and OCR.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Artificial Intelligence in Automated Bookkeeping: A Value-added Function for Small and Medium Enterprises

Azman

Mohamed

Jamil

2021

JOIV : Int. J. Inform. Visualization

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Bookkeeping plays a vital role in dealing with records of day-to-day financial transactions from invoices until payment. It is also a method of documenting all company transactions in order to create a collection of accounting documents. Studies show that an evolution of bookkeeping management from manual record keeping to electronic record keeping had simplified most burden of bookkeepers as well as more reliable and accurate. Bookkeeping includes, in particular, classifying items correctly and entering financial details into an accounting system. However, with the rise of artificial intelligence, automated bookkeeping system is common to large businesses tasks at real time with hassle free. The system will function more than just journal management but also a decision-making tool to any businesses. Despite the benefits of the system, many small and medium enterprises especially in Malaysia still hesitate to implement the system. Artificial intelligence will further improve automated bookkeeping making it simpler and efficient for all levels of businesses. This paper presents an Artificial Intelligence perspective and methods used in automated bookkeeping focuses on invoices processes such as Optical Character Recognition (OCR), for document recognition, machine learning and auto journal record entries. Besides that, its challenges to be implemented in small and medium enterprise. The result of these studies highlighted benefits in the automated bookkeeping process to suit Malaysian small and medium enterprises. Future work will look at the suggested intelligence features to be implemented for a more efficient automated bookkeeping for small and medium enterprise.

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Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Artificial Intelligence in Automated Bookkeeping: A Value-added Function for Small and Medium Enterprises

Azman

Mohamed

Jamil

2021

JOIV : Int. J. Inform. Visualization

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“…The next closest research area focuses on information extraction from invoices. The area is connected with optical character recognition (OCR) and copy machine producers (Ha et al, 2018). Besides textual data, other layout and visual features are extracted from a document (Ha, 2017; Ha et al, 2018; Palm et al, 2017.…”

Section: Related Workmentioning

confidence: 99%

“…There are studies aimed at invoice detection among other business documents (Ha, 2017), or the detection of invoice type (handwritten, printed, receipt) (Tarawneh et al, 2019). The identification of the right blocks on an invoice that include the price field by a combination of visual and text features has reached an accu racy of 80% (Ha et al, 2018), and F1 of 84% with the use of recurrent neural networks (Palm et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

The Process of Unit Price Extraction from Public Sector Contracts

Bruckner¹,

Vencovský²

2020

AIP

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“…The EoIs that assigned to the decoders are Ticket Number (TCN), (Starting Station (SS), Train Number (TAN), Destination Station (DS)), Date (DT), (Ticket Rates (TR), Seat Category (SC)), and Name (NM). In passport scenario, we design five decoders to cover seven EoIs, decoding steps of each decoder are 25, 5,15,35,35. The EoIs assigned to the decoders are Passport Number, Name, (Gender, Birth Date), Birth Place, (Issue Place, Expiry Date).…”

Section: B Experiments Settingmentioning

confidence: 99%

“…"China" or "USA" for the entity "Country", "Jack" or "Rose" for the entity "Name". Previous approaches [3]- [5] mainly adopt two steps, in which text information is extracted firstly via OCR (Optical Character Recognition), and then EoIs are extracted by handcrafted rules or layout analysis.…”

Section: Introductionmentioning

confidence: 99%

EATEN: Entity-Aware Attention for Single Shot Visual Text Extraction

Qin

Liu

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical detection and recognition paradigm. This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-end trainable system to extract the entities without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of entity extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three realworld scenarios (train ticket, passport and business card), which is publicly available at https://github.com/beacandler/EATEN. To the best of our knowledge, EATEN is the first single shot method to extract entities from images. Extensive experiments on these benchmarks demonstrate the state-of-the-art performance of EATEN.

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Recognition of OCR Invoice Metadata Block Types

Cited by 10 publications

References 10 publications

Artificial Intelligence in Automated Bookkeeping: A Value-added Function for Small and Medium Enterprises

Artificial Intelligence in Automated Bookkeeping: A Value-added Function for Small and Medium Enterprises

The Process of Unit Price Extraction from Public Sector Contracts

EATEN: Entity-Aware Attention for Single Shot Visual Text Extraction

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