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Cloudwalk has made a number of significant functional breakthroughs in this field:
The model can automatically search and mine features. With the search strategy manually configured, the model is able to automatically process and screen features to find the most effective feature combination, and apply the automatic feature engineering to mine feature combinations and bin the features, without manually selecting the features and adjusting the binning. Users only need to define the search range of model parameters, and the algorithm can automatically search and adjust the optimal model parameters. Using the decision-making tree for feature extraction, this model is characterized by strong nonlinear feature fitting ability. Experiments on multiple datasets show that with an effect similar to that of other models established based on the decision-making tree algorithm, e.g. LGB and XGB, this model can output the risk value corresponding to each feature and feature combination and then deploy these values through the decision-making engine.

The original face recognition algorithm developed by Cloudwalk possesses industry-leading advantages in face detection, face comparison, 1:N face recognition and face attributes.

The Cloudwalk Pixer-Anchor text detection algorithm can identify and structurally output text information on test sets with multiple formats and interference such as complicated background, shadow, crease, seal, watermark, serial and dislocation. In ICDAR2015 and 2017 MLT, this algorithm refreshed the world record in detection accuracy and efficiency, winning the first place in many POC tests in the banking industry.
With automatic recognition training algorithm, automatic adaptation and reconstruction algorithm, automatic tagging and correction algorithm, text detection and format analysis algorithm as well as technologies such as semantic understanding and automatic integration, the Cloudwalk OCR automatic training platform automatically tags and trains pictures, and produces a text recognition model that can be used in the real environment without basic data, realizing “tagging while training”.