To automatically obtain invoice information and store relevant information in the form, you can achieve the following steps: Data acquisition: For electronic invoices, you can automatically receive them directly through system docking or email.

For paper invoices, they can be converted into digital images using methods such as scanners or mobile phone photos.

Information extraction: Using OCR (Optical Character Recognition) technology to extract text information from invoice images.

OCR technology recognizes printed or handwritten text and converts it into editable text.

The extracted text is further processed and parsed through regular expressions, natural language processing, or machine learning algorithms to identify and isolate key information in the invoice, such as invoice number, date, supplier, item details, quantity, unit price, total price, etc Data collation: The extracted invoice information is sorted according to the predefined table structure, ensuring that each piece of information corresponds to the correct column.

For unrecognized invoices or invoices with incomplete information, you can set up an exception handling mechanism, such as manual review or marking as pending.

Data storage: Store the organized invoice information in a spreadsheet, such as Excel, CSV, or other database format.

You can set automatic saving periodically or trigger saving operations when the number of invoices reaches a certain threshold.

Verification and review: Verify and review the invoice information automatically extracted and stored in the form to ensure the accuracy and completeness of the data.

It can be done by comparing with other system data, setting up business rule verification or manual sampling audit.

System Integration and Automation: Integrate this invoice information extraction and storage process with your existing financial system, ERP system, or other related systems.

Through API interface or middleware and other technologies to achieve automatic data synchronization and update, to improve work efficiency and reduce manual input errors.

Continuous optimization and improvement: Continuous optimization and improvement of the OCR recognition algorithm and data processing process according to the actual application situation.

Periodically evaluate recognition accuracy and adjust relevant parameters to improve performance.

Please note that the exact implementation may vary depending on enterprise needs and system environment.

In practice, it is recommended to select the appropriate OCR technology provider and data processing tool according to the specific situation to complete the above process.