How to Extract Data from Purchase Orders With AlgoDocs Purchase Order OCR. A Comprehensive Guide For 2025

Purchase orders (POs) are critical documents in business operations. They ensure clear communication between buyers and sellers, detailing the goods or services, pricing, and other contractual terms. With the rise of digitization, extracting data from purchase orders efficiently has become essential for modern businesses. Both b2b and b2c business scenarios use a purchase order to facilitate clear communication between two parties. It helps in maintaining important information related to the transaction of goods between two parties. This guide explores how to extract data from purchase orders, focusing on tools like purchase order OCR and strategies to streamline the process.

A purchase order (PO) is a written document issued by a buyer to a seller that indicates the type, quantity, and agreed price for products or services by two parties. It plays a very crucial role in ensuring smooth procurement operations and is a legally binding document once accepted by the seller. In simple terms, a purchase order (PO) document describes what goods have been requested by the buyer to sellers to confirm their interest in procuring goods and services.

Key Elements of a Purchase Order 

• PO Number: A unique identifier. 
• Buyer and Seller Information: Contact details and addresses. 
• Order Details: Description, quantity, and price of items. 
• Delivery Terms: Shipping method, destination, and date. 
• Payment Terms: Agreed payment timelines and methods. 

Manually managing this information is time consuming, especially for businesses handling large volumes of POs.

Relying on manual methods to extract and process data from purchase orders leads to many challenges for businesses.

Time Consuming: Extracting data from a purchase order manually is a time-consuming task. If you have to extract data from a single page document, then manually it can take hours, but using a purchase order app for data extraction only takes 5 to 10 minutes.

Error Prone: One of the biggest challenges with manually extracting data from a purchase order is that it is prone to error. The most common human errors, such as typos or missed fields, can disrupt workflows and lead to costly mistakes. Due to the high volume of data, errors tend to happen if data is being extracted with manual method.

Scalability Issues: As businesses grow, manually processing hundreds or thousands of purchase orders becomes unsustainable. The manual processing of 100 purchase order documents can slow down the productivity of an organization, which can lead to other challenges, such as loss of profit and time.

Resource Intensive

Manual methods require significant labour, which leads to a rise in operational costs. To overcome these challenges, businesses are turning to automation tools like purchase order OCR technology. Since the purchase order OCR technology requires very little or no human intervention.

Purchase Order OCR (Optical Character Recognition) is a technology that captures and converts a printed or handwritten text into machine-readable data. This type of OCR technology is built and trained to capture and extract data such as vendor name, buyer name, date, amount, address, and other information from a PO document. You can save this extracted data in Excel, CSV, JSON, or another file format. With the help of purchase order OCR technology, businesses can significantly save time and money and scale their business to greater heights. The advent of AI OCR tools such as AlgoDocs Purchase Order OCR has made data extraction from purchase orders more accurate and productive.

Purchase Order OCR (Optical Character Recognition) is a technology that streamlines the extraction of information from purchase orders. It works by scanning documents in various formats—such as PDFs, images, or scanned papers—and recognizing text to extract key data like order numbers, item descriptions, quantities, prices, and delivery dates. The data is subsequently organized into a structured format, facilitating seamless integration into procurement systems. Automating this process allows Purchase Order OCR to decrease manual data entry, reduce errors, and improve the efficiency of the procurement workflow.

Scanning Documents: Paper POs are digitized using scanners. 

Data Recognition: The OCR engine identifies text fields such as PO numbers, item descriptions, and quantities. 

Data Extraction: The software extracts relevant fields and organizes them into a structured format. 

Integration: The extracted data is transferred into systems like ERP or accounting software.

Algodocs is an AI-based purchase order OCR app that extracts data from PO documents. AlgoDocs uses AI technology to automate data extraction from pdfs, scanned documents, and handwritten notes. You can build your own extractor in Algodocs and train the AI model to capture, analyze, and process the data from a document. You can integrate Algodocs with third-party apps for document processing and data extraction.

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A purchase order OCR (Optical Character Recognition) app is a highly beneficial tool for industries of all types. Whether you are operating in a B2B (Business-to-Business) or B2C (Business-to-Consumer) environment, implementing a PO OCR solution can significantly enhance productivity and drive business growth. However, certain industries stand to gain the most from the adoption of OCR technology:

Retail and E-commerce: In the fast-paced world of retail and e-commerce, businesses generate a high volume of purchase order documents daily. As customer transactions increase, so does the number of PO document requests. In such scenarios, OCR technology is invaluable for efficiently processing and managing data from these documents. It automates the extraction of key information, reducing manual effort and minimizing errors, ultimately leading to smoother operations and improved customer service.

Manufacturing: The manufacturing sector handles an immense amount of data regularly, including invoices, purchase orders, and various other types of documents. Manually capturing, extracting, and organizing this data can be inefficient and prone to errors. By leveraging OCR technology, manufacturing businesses can achieve significant efficiency gains and streamline their data processing workflows. With the high volume of purchase orders typical in this industry, a purchase order OCR app can resolve many challenges related to data extraction, enabling better resource management and operational efficiency.

Healthcare: The healthcare industry deals with extensive documentation, including numerous purchase order documents daily. Accurate and swift data extraction is crucial for maintaining operational efficiency and ensuring compliance with regulatory standards. Purchase order OCR tools are particularly beneficial in this context, as they can handle large volumes of documents with speed and precision, reducing the likelihood of errors. This capability is essential for healthcare providers to maintain accurate records, manage supplies effectively, and ensure timely procurement of necessary goods and services.

AlgoDocs: AlgoDoc’s Purchase Order OCR is a feature-rich, AI-based tool for extracting data from purchase orders. It uses AI and OCR technology to capture, extract, and process data from PO documents. The best part of AlgoDocs is that it leverages AI and ML models for data extraction, resulting in faster and more accurate data retrieval. You can integrate the AlgoDocs AI OCR app with third-party tools and easily save, import, and export the data. Try AlgoDocs’ free-forever plan on our website.

Docsumo: Docsumo is a decent OCR app for extracting data from purchase order documents. They offer customized data extraction solutions to their customers. You can try Docsumo’s PO OCR from their website.

Nanonets:

Nanonet like other OCR apps, Nanonets is a great tool for purchase order data extraction. Nanonets offers integration with third-party apps for data integration. However, the functionality of Nanonets is limited, and users might sometimes get confused with its UI.

Parseur: Parseur is a great alternative if you are looking for a reliable purchase order OCR app. Parseur offers a free plan as well, but the options are very limited if you want to try their app.

Veryfi: Veryfi is a good app with decent features for extracting data from purchase orders. However, it lacks many modern features. Veryfi offers a free plan with limited features on their platform.

Extracting Purchase Order Data Using AlgoDocs.

Sample Document

Step-by-Step: How to extract Purchase order data Using AlgoDocs?

Step 1: Login to your Algodocs account and go to the home page which is the Dashboard.

Step 2: Click on the Extractor tab , and you will notice on the right side of the Extractor tab, the populated option to choose what kind of extractor you want to create.

Step 3: Click on Custom, and it will pop up a new window to name the extractor.

Step 4: Upload the sample PDF file, then click on Create Extractor. The Window will close, and you will be able to see your extractor in the extractor lists below,

Step 5: Click on the Manage tab , and you will be taken to the field/table creation page.

Step 6: Click on the +Add , and it will show the extraction methods options.Step 7: Click on Form Data Extraction, this will launch a new window preview the sample PDF

document you uploaded. Click on Continue ,this will open a new window with all the detected table and its values AlgoDocs AI.

Step 8: Use the Keep Rows Filter to keep the PO #:.

Step 9: Use the Alter Columns Filter, and select Remove Specific Column Filter to remove column 1

Step 10: Then convert the value in the remaining column to text, by selecting the Convert to Text filter.

Step 11:   Now we can duplicate the field PO # field for “Purchase Order To” because they can both work with “Form data”.

Step 11b:   Now we can give the field a name. “Purchase Order To” and click the “Duplicate”.

Step 12:   Change the Text in the Value field to “Purchase Order To”

Step 13: Now follow the same steps used in “Purchase Order To” for “Ship To Field”

Step 14: Now add a new Field and Select Table Extraction under Rule-Based Data Extraction

Step 15: Align the column separators accordingly and use the add columnStep 15: Align the column separators accordingly and use the add column

Step 16: Use the keep section filter to eliminate data that is not part of the table contents.

Step 17: WeUsed the Condition option, start section where column 2 contains Service Description and end section where column 4 contains TOTAL SERVICES: , then we checked the “Exclude this row” checkboxes.

Step 18: Next, we keep rows where column 1 contains a value, as this keeps just the rows with data.

Step 19: Next, we remove blank spaces from all columns.

Step 20: Now we set Column Headers.

Step 20b: Save this field “Services” then duplicate it for “Materials” Table

Step 21: Now make adjustments to the “Keep Section” “with Condition” Filter to capture materials data.

Step 22: Set Column Headers for the Materials table, then name and save the field.

Step 23: Now duplicate the “Ship To” Field for the “Total Job Cost” field and adjust the filters to capture the data.

Step 24: With this done, we can save and exit the extractor editor.

Step 25: Now we head to the extracted data section and select the extractor from the extractor lists. We have the option of single export to EXCEL, JSON, CSV, and XML.

Here is the EXCEL OUTPUT

Conclusion  

Extracting data from purchase orders is no longer a tedious task with the advent of automation and OCR technology. By leveraging tools like purchase order OCR, businesses can save time, reduce errors, and improve operational efficiency. 

Whether you’re a small business or an enterprise, adopting automated solutions for PO processing is a step toward a more streamlined and scalable future. Start optimizing your purchase order workflows today and experience the transformative benefits of automated data extraction. 

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