Enhance image preprocessing capabilities through the following technologies to improve recognition accuracy:

• Trapezoidal correction:When capturing or scanning documents, images may exhibit trapezoidal distortion (e.g., when a document is photographed from an angle). Through trapezoidal correction algorithms, the image is automatically adjusted to its correct rectangular shape, preserving the accuracy of the text structure and further enhancing OCR recognition effectiveness.
• Automatic cropping :Automatically detects the edges of the document and crops out unnecessary background areas. This helps focus on the text content, minimizing background distractions and improving OCR results.
• Image rectification :Through precise geometric correction methods, distortions or tilts in the image are eliminated, restoring its true structure and visual appearance.

• Provides a RESTful API for external applications to access recognition services, making it easy to integrate OCR recognition into existing workflows.

• Rapid integration:When calling the API, simply input the image and document code. After the API completes the recognition, it will return the results in JSON format.

• Real-time service:Through the API, external applications can submit documents in real-time and receive OCR results, enabling the fast processing of large volumes of documents and enhancing the automation efficiency of businesses.

By integrating large language models for content extraction from recognition results, there is no longer a need for extensive document data for training when handling non-fixed structure documents.

• Provides a customizable document recognition setup feature, allowing users to define the field names to be extracted. Real-time online testing is available, and if the results meet expectations, they can be saved for future use in document uploads and recognition.
• By integrating large language models, the system automatically recognizes semantically similar field names (e.g., birthday, date of birth, birthdate) during recognition and extraction. As a result, there is no need to collect a large number of samples for testing and training during setup.