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.

• Offers a customizable document recognition configuration feature, enabling users to add document types for recognition, define the specific fields to be recognized, and perform real-time testing. If the results align with expectations, the settings can be saved for future use during document uploads.
• During the recognition and extraction process, the system automatically identifies fields with semantically similar labels (e.g., Date of Birth, Birth Date, Birth Year and Month, etc.). As a result, there is no need to upload sample data for training in order to complete the configuration.