Picture recognition method and apparatus, electronic device, and medium

A computing device, comprising a processor and a memory, the memory storing instructions that, when executed by the processor, cause the computing device to perform a picture recognition method including:

• while recognizing text for an acquired image through an optical character recognition model:
• obtaining, based on the acquired image, a current string using the optical character recognition model;
• computing a hash value of the current string;
• storing the current string and the hash value of the current string into a first preset tree structure;
• predicting a new probability value of the current string at a next moment;
• storing the hash value of the current string into a second preset tree structure;
• generating, based on the current string, a target character set using the optical character recognition model at the next moment;
• extending the current string through the target character set to obtain an extended string set;
• for each extended string in the extended string set:
• computing a respective probability value and a respective hash value;
• and
• storing the respective probability value and the respective hash value into the second preset tree structure;
• selecting N strings with one or more highest probability values from the first preset tree structure according to the hash values stored in the second preset tree structure, N being an integer not less than 1;
• and
• using the N strings as current strings, repeating the foregoing steps to obtain a target string with a highest probability value as a final recognition result.

A non-transitory computer-readable storage medium, comprising instructions that, when executed by a processor of a computing device, cause the computing device to perform a picture recognition method including:

• while recognizing text for an acquired image through an optical character recognition model:
• obtaining, based on the acquired image, a current string using the optical character recognition model;
• computing a hash value of the current string;
• storing the current string and the hash value of the current string into a first preset tree structure;
• predicting a new probability value of the current string at a next moment;
• storing the hash value of the current string into a second preset tree structure;
• generating, based on the current string, a target character set using the optical character recognition model at the next moment;
• extending the current string through the target character set recognized at the next moment, to obtain an extended string set;
• for each extended string in the extended string set:
• computing a respective probability value and a respective hash value;
• and
• storing the respective probability value and the respective hash value into the second preset tree structure;
• selecting N strings with one or more highest probability values from the first preset tree structure according to the hash values stored in the second preset tree structure, N being an integer not less than 1;
• and
• using the N strings as current strings, repeating the foregoing steps to obtain a target string with a highest probability value as a final recognition result.