A Deep Learning-Based Approach for Named Entity Recognition on Commercial Receipts

System that uses OCR (Optical Character Recognition) to extract data from invoice photos (e.g. products, supermarket, prices), and displays it in a dashboard.

Context A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides. Acknowledgements Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and Relevant Papers: Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018. Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text

使用FastAPI构建发票识别系统后端服务，支持并发。使用ERFNet模型训练发票轮廓检测，进行畸变矫正，OCR识别，模板匹配，支持倾斜发票识别。准确率99.9%。

Invoice and train ticket recognition system based on deep learning

SMART Invoice Detector built using AI with automated recognition and ocr.

In 100 days i will an OCR invoice recognition software

An Optical Character Recognition (OCR) preprocessing toolkit for invoices

Optical character recognition (OCR) allows you to extract printed or handwritten text from images, such as photos of street signs and products, as well as from documents—invoices, bills, financial reports, articles, and more. Microsoft's OCR technologies support extracting printed text in several languages.

It is a comprehensive web application designed to streamline invoicing and payment processes. With advanced features such as facial recognition authentication, real-time updates, and QR code generation, it offers a secure and efficient solution for businesses to manage their billing needs. Developed using MERN.

Assignment By - MASTERS INDIA Project - Extract invoice number, invoice date, line items from invoice images. Project Details - MySelf Aditya, After my research toward this assignment, i found, this is the problem of OCR (OPTICAL CHARACTER RECOGNITION) Basically the work of OCR is to transform & extract the data from semi-structured(BILLs, INVOICES) or un-structured(CONTRACT, LEGAL DOCUMENTS) to structured format(CSV, EXCEL, XML, DATABASES). By this project i got idea about REAL-LIFE Problem, the problem of entire enterprise are data entry. Data entry is preety expensive & time consuming So, basically OCR creates an environment without manual data entry. 90% of stuffs can be configure by OCR & humans will only interrupt when accuracy is not good or some intervention is required. TOOLS of OCR are available in the market like- ABBYY, ROSSUM, AUTOMATION ANYWHERE, XTRACTA for the sake of Assignment, I did this Project with the help of CV2 (Computer Vision) and PYTESSERACT (python library for OCR). ''' ''' the main problem with this project is that we dont have any similar kind of format or structure, [things can be done- REGEX or ROI] initially i was looking for regex to solve this problem, but again due to variation in every format, i can just fetch the invoice date automatically by regex. if i show you the images, we can fetch the each given date by REGEX then store it in list, we know that invoice date will be store firstly & be the index 0. And hence we can easily fetch 0th index element every time & append it in CSV. But the problem arrives in remains 2 that we don't have any common starting point or end point hence we can't apply regex here......

发票识别信息提取

Project to implement the Optical Character Recognition technology to extract the invoice details from images of invoice receipts.

Optical Character Recognition System for Paper Invoices

An OCR - Optical Character Recognition that can extract financial data from images of invoices.

An Optical Character Recognition (OCR) application using Blazor and Azure Document Intelligence with prebuilt-invoice model

A Python analysis using YOLO V4 for object detection and Tesseract OCR for text recognition. This custom OCR system automates invoice scanning by detecting and extracting key fields like Invoice number, Billing Date, and Total amount, streamlining the process of digital invoice reconciliation.

The Invoice Fraud Detection System is a comprehensive tool designed to analyze invoices for potential fraud. It uses advanced techniques such as OCR (Optical Character Recognition), pattern matching, and risk scoring to identify anomalies in invoices. The system supports multiple formats, including PDFs and images, and provides detailed reports.

Invoice recognition in iOS with openCV

[教学向] 使用简单的图片处理对发票进行识别

Invoice Smart Recognition System

No description available

A modern, intelligent invoice processing system with advanced multi-format data extraction capabilities. Process invoices from PDFs, Excel files, and images with smart data recognition.

This project integrates Optical Character Recognition (OCR) technology with Language Models (LM) to enhance the extraction of text from French invoices. By leveraging the capabilities of OCR to capture text from scanned documents and the contextual understanding provided by Language Models

This repository contains code for extracting invoice details from PDFs using Optical Character Recognition (OCR) and object detection using the YOLOv8 model.

An AI-powered Named Entity Recognition (NER) system that automatically extracts key information from invoice or bills images using OCR and a custom-trained spaCy model.

Processing scanned invoices at scale requires extracting structured data from unstructured images. Production OCR pipelines must handle image preprocessing, text recognition noise, regex-based field extraction, and line-item parsing with confidence scoring.

A comprehensive web-based tool for delivery teams to process invoice images using OCR (Optical Character Recognition), manage daily deliveries, capture proof of delivery photos, collect digital signatures, and export data for reporting.

Easy Finance：批量识别增值税发票与银行回单，基于腾讯云API和Reflex；Batch recognition of VAT invoices and bank slips, built based on Tencent Cloud API and Reflex.

A tool for classifying and sorting fiscal and non-fiscal documents using OCR (Optical Character Recognition) based on Portuguese keywords. Designed to organize documents such as receipts, invoices, and payment proofs, making it easier for users to perform further analysis.