In this article, I cover the theory behind receipt digitization and implement an end-to-end pipeline using OpenCV and Tesseract. I also review a few important papers that do Receipt Digitization using Deep Learning. In order to manage this information effectively, companies extract and store the relevant information contained in these documents. Traditionally this has been achieved by manually extracting the relevant information and azure sentinel api it into a database which is a labor-intensive and expensive process.
Extracting key information from receipts and converting them to structured documents can serve many applications and services, such as efficient archiving, fast indexing and document analytics. They play critical roles in streamlining document-intensive processes and office automation in many financial, accounting and taxation areas. Computing Accounts payable AP and Accounts Receivables ARs manually is costly, time-consuming and can lead to confusion between managers, customers and vendors.
With digitization, companies can eliminate these drawbacks and can have more advantages - Increased Transparency, Data Analytics, Improved working capital and easier tracking.
Managing tasks, information flows, and product flows is the key to ensuring complete control of supply and production. This is essential if organizations are to meet delivery times and control production costs. The companies that are truly thriving these days have something significant in common: a digitized supply chain. One of the key elements of realising the next generation digital Supply Chain 4. Manual entry of receipts acts as a bottleneck across the supply chain and leads to unnecessary delays.
If this receipt processing is digitized it can lead to substantial gains in time and efficiency. Have an OCR problem in mind? Want to digitize invoices, PDFs or number plates? Head over to Nanonets and build OCR models for free! Receipt digitization is difficult since receipts have a lot of variations and are sometimes of low quality.
Scanning receipts also introduces several artifacts into our digital copy. These artifacts pose many readability challenges. The first step of the process is Preprocessing. Most scanned receipts are noisy and have artefacts and thus for the OCR and information extraction systems to work well, it is necessary to preprocess the receipts.
Common preprocessing methods include - Greyscaling, Thresholding Binarization and Noise removal. This can be achieved by thresholding, which is the assignment of pixel values in relation to the threshold value provided.
Each pixel value is compared with the threshold value. If the pixel value is smaller than the threshold, it is set to 0, otherwise, it is set to a maximum value generally The next step in the pipeline is OCR. It is used to read text from images such as a scanned document or a picture. This technology is used to convert, virtually any kind of images containing written text typed, handwritten or printed into machine-readable text data.
Applying filters and image classification is pretty straightforward, think MNIST Classification using SVNbut contour matching is a very difficult problem and requires a lot of manual effort and is not generalizable. Next come the Deep Learning approaches.The problem of optical character recognition OCR in various conditions remains as relevant today as it was in past years.
Automated recognition of documents, credit cards, recognizing and translating signs on billboards — all of this could save time for collecting and processing data. With the development of convolutional neural networks CNN and methods of machine learning, the quality of text recognition is continually growing.
Having implemented a project for receipt recognition, we once again saw how effective convolutional neural networks are. For our research, we chose different receipts from a range of Russian stores with the text using both Cyrillic and Latin letters.
The developed system can be easily adapted for recognizing receipts from other countries and text in other languages. The goal of our project is to develop an app using the client-server architecture for receipt recognition and extracting meaning from receipts. The preprocessing stage consists of the following preliminary work with the image: finding a receipt in the image, rotating the image so that the receipt strings are located horizontally, and then making a binarization of the receipt.
Rotating image to recognize a receipt In order to solve the problem of finding a receipt in the image, we used the following methods:. The problem here was reduced to finding an area in the image that contains a complete receipt and minimum background. In order to make the finding process simpler, we first rotate the image so that each individual row is as close to horizontal as possible Image 2. The turning algorithm is required to maximize the variance of the brightness sum over the strings.
The maximum is reached when the strings are located horizontally. This function does adaptive binarization with a high threshold and leaves white pixels in areas with a high gradient, whereas areas that are more homogeneous become black.
Using this function, only a few white pixels remain with a homogeneous background and we look for them in the described rectangle. As a result, the derived rectangle includes the receipt area and a minimum of unnecessary background. We decided to find keypoints of the receipt using a convolutional neural network as we did it before for the object detection project. We chose the receipt angles as keypoints.
This method performed well, although it was not as efficient as adaptive binarization with a high threshold. The convolutional neural network showed a non-ideal result because it was trained for predicting the angle coordinates only relative to the found text. As an alternative to the described models, we decided to try a Haar cascade classifier. Even the CNN performed with higher quality.
The window is quite big so that it contains the text as well as the background. The first stage of finding the text consists of finding the connected components. We did this using the findContours function from OpenCV. The majority of connected components are real characters, but some of them are just noise fragments that are left after binarization. After this the characters are combined to form words by searching their closest neighbors.Receipt recognition is a specific kind of document processing.
Location of data fields is not fixed, but depends on the country where the receipt was printed and the issuing organization. As receipts are often printed using small fonts on low quality paper, and the pictures are made with a mobile phone instead of scanned, special preprocessing is required before OCR.
All these conditions make data capture and recognition more complicated. This information will be necessary to access the processing server, see Authentication. To recognize receipts, use the processReceipt method with recognition parameters suitable for your image:. Call the processReceipt method with the specified parameters.
A new processing task will be created on the server. Start free trial Sign in Contact sales. Home Documentation Quick start guides How to recognize receipts How to recognize receipts. Several names of countries should be separated with commas, for example "taiwan,china". Specify if the image is a photograph or a scanned image via the imageSource parameter. This affects the preprocessing operations which can be performed with the image such as automatic correction of distorted text lines, poor focus and lighting on photos.
In this case the image source will be detected automatically. Specify if the skew or the orientation of the image should be automatically detected and corrected.
Applying OCR Technology for Receipt Recognition
Further details can be found in our Privacy Notice.Comment Optical Character Recognition is a process when images of handwritten, printed, or typed text are converted into machine-encoded text. Automated recognition of documents, credit cards, car plates and billboards significantly simplifies the way we collect and process data. We used CNN in our research to recognize paper receipts from retail stores.
The system can be adjusted to process different languages, but we tested it using Russian. The goal of our project was to develop an app using the client-server architecture for receipt recognition. Let's take a closer look, step by step.
Deep Dive Into OCR for Receipt Recognition
First things first: we rotated the receipt image so that the text lines were horizontally oriented, made the algorithm detect the receipt, and binarized it.
First, we recognized the area on the image that contains the full receipt and almost no background. To achieve this, we rotated the image so that text lines are horizontally oriented. This function keeps white pixels in areas with a high gradient, while more homogeneous areas turn black.
Using this function, we got a homogeneous background with a couple of white pixels. We were searching for them to define the rectangle. We decided to find receipt keypoints using a convolutional neural network as we did before for the object detection project. We chose the receipt angles as key points. This method performed well, but worse than adaptive binarization with a high threshold.
The CNN was able to define only those angle coordinates relative to the found text. Text to angle orientation varies greatly, meaning this CNN model is not very precise.
As a third alternative, we tried the Haar cascade classifier. Even the CNN performed much better. The window is quite big so that it contains the text as well as the background.
The majority of connected components are characters, but a few are just noisy text fragments left after binarization. The characters are then combined into words via a nearest neighbour search.
Here is the principle of nearest neighbours method.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
If nothing happens, download the GitHub extension for Visual Studio and try again. Accompanying post. This is a single page Xamarin Forms based cross platform mobile application which takes an image and calls the partner Agitare api to store it in Azure blob storage. Once we get a stored URL to the image, we then pass this URL to Machine learning web service end point to obtain probability scores of different classification groups. The results are shown in a CardViewCell control in a Xamarin forms page.
The app uses MVVM pattern to implement this functionality. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. No description, website, or topics provided. C Python. Branch: master.
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Reload to refresh your session. You signed out in another tab or window. Feb 16, The recognition of receipts is a tricky OCR scenario:. Advanced PDF Processing. Document Classification. General Features. Technology Cycles.
OCR Software and Solution for Receipt Recognition
Knowledge Base. Log In. Table of Contents Receipt Recognition. Receipt Recognition Business Development. The recognition of receipts is a tricky OCR scenario: Recognizing the text is often a challenge, because of often bad printing quality and the used fonts.
There might not be clear separation between keywords and data as you might see it in other Data Capture Scenarios. This text type it is not concerned with the actual font of the text. It rathers informes the recognizer that there may be text of low quality, mostly in monospaced or normal font. The typical receipt text can look like this:.
It is as well possible to apply standard OCR on receipts and then parse and analyze the results with own technologies and algorithms. A method for receipt recognition. Output is an XML of special format:.
Automating Receipt Digitization with OCR and Deep Learning
Saving and processing these receipts can be a tiresome and long drawn out affair, as many of us do not have the time to transfer data from receipts every day. So we often end up with a pile of them after a trip and have to manually sort them. Here is where software that OCR receipts comes in handy as they scan and process a large pile of receipts automatically. Software that OCR receipts also does away with many burdens associated with processing of receipts, such as manual sorting.
Software packages that OCR receipts are designed to work with a portable scanner which can be taken along on a business trip. Cheap and easy to assemble this scanner can become operational within a few minutes, and start scanning receipts and even business cards. Once the scanning process is complete, software that OCR receipts automatically organizes the scans in folders.
Once the scanning of receipts is done, software that OCR receipts take over and capture important information from receipts. Software packages that OCR receipts, are built on optical character recognition technology which allows them to capture printed text from receipts.
Sometime they even have advanced technologies such as intelligent character recognition ICR. When ICR is included in such software, they can even capture handwriting and convert them into text. It can even synchronize information from scanned business cards with contact information on Microsoft Outlook.
However, software that OCR receipts do not capture all the text present on receipts. In fact they cannot perform OCR on the entire contents of a scanned page.
Instead they only capture certain important information such as establishment name, tax and amount paid. Once captured from several receipts, this information can be totaled according to category of expense, such as meals, travel expense etc. Download Contact Store. OCR Receipts.
More About Software that OCR Receipts Receipts very often need to be saved as part of expense reports, especially if we go on business trips sponsored by companies. Benefits Provided by Software that OCR Receipts Software packages that OCR receipts, are built on optical character recognition technology which allows them to capture printed text from receipts.