In recent years, the recognition ability of OCR has been constantly breaking through, gradually playing a role in more complex digital scenes, effectively reducing the labor cost of information extraction and input, and helping enterprises focus on upper-level business applications. Baidu EasyDL Zero-threshold AI development platform has been committed to creating a zero-algorithm basis, everyone available AI development platform, reduce the threshold of SMALL and medium-sized enterprises to apply AI, so that small and medium-sized enterprises that do not understand AI algorithm or AI underlying infrastructure is not perfect can also obtain high-quality AI services. Baidu EasyDL OCR self-training platform, which was newly launched in 21 years, provides a high-precision word recognition model production platform, one-stop to meet the multi-scene, high-precision OCR customization needs of enterprises, and can be widely used in express logistics, finance and tax reimbursement, insurance and medical treatment and other typical industry scenes. EasyDL OCR Self-training platform has four product advantages:
1. Zero threshold for operation. Users can also complete model training in a one-stop process without algorithmic foundation;
2. High precision of model. The pre-training model based on Baidu’s rich experience in commercial model training, supplemented by self-developed entity detection model, ensures that the average accuracy of output model can reach more than 90%.
3. Low cost of data processing. Combined with intelligent detection and recognition technology, the efficiency of data annotation is greatly improved, and the virtual data generation and data disturbance function, fast and low-cost supplementary training data, enrich the data scene;
4. Flexible service deployment. After the model training is completed, it can be deployed in the cloud with one click to quickly generate standard API interfaces for invocation. In addition, the model or complete training platform produced by training can be privatized and deployed locally to meet the data privacy requirements in specific scenarios.