Handwriting recognition method, computer device and readable storage medium

Abstract

The application discloses a handwriting recognition method, a computer device and a readable storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: in response to the input of a first multi-modal feature and a historical handwriting template of a target author, a machine learning model is called to generate a recognition result of whether a to-be-recognized author is the target author; wherein the historical handwriting template comprises local stability of handwriting corresponding to a plurality of local writing units and a second multi-modal feature, and the local writing units comprise a character, a partial component or a character block; the local stability is used to generate a local attention weight corresponding to each local writing unit, and the local attention weight is used to perform weighted processing on the similarity of the first multi-modal feature and the second multi-modal feature during inference of the machine learning model. The application can improve the accuracy of recognizing whether the author is the target author.

Claims

1. A handwriting recognition method, characterized in that, The handwriting recognition method includes: In response to the input of the first multimodal feature and the target author's historical handwriting template, a machine learning model is invoked to generate an identification result as to whether the author to be identified is the target author; The historical handwriting template includes local stability and second multimodal features corresponding to multiple local writing units. The local writing units include characters, radicals, or character blocks. The local stability is used to generate local attention weights corresponding to each local writing unit. The local attention weights are used to perform weighted processing on the similarity between the first multimodal features and the second multimodal features during the inference of the machine learning model. The first multimodal features are extracted from the first local writing unit image, which is obtained by segmenting a first image. The first image includes the text written by the author to be identified, and each first local writing unit image corresponds to one local writing unit.

2. The handwriting recognition method according to claim 1, characterized in that, The recognition result is generated based on the local matching score. Calculated based on the following formula: Among them, the Representing a local writing unit The corresponding local attention weights; Indicates the mode in the second multimodal feature In local writing units Modal weights in; the Indicates the mode in the first multimodal feature In local writing units The first feature in; the Indicates the mode in the second multimodal feature In local writing units The second feature; This represents the similarity function.

3. The handwriting recognition method according to claim 2, characterized in that, The w(r) is calculated using the following formula: Among them, the Indicates An exponential function with base 0; the The index variable represents all local writing units; Representing a local writing unit Attention score; Representing a local writing unit Attention score; It is calculated using the following formula: Among them, the , These are the query transformation matrix and the key transformation matrix, respectively, both of which are learnable parameters of the machine learning model; Represents the feature dimension; the Represents the fluctuation penalty coefficient; the This indicates that the first multimodal feature is in the local writing unit. The embedded aggregation results on; This indicates that the second multimodal feature is in the local writing unit. The embedded aggregation results on; Representing a local writing unit The local fluctuation aggregation value; It is calculated using the following formula: Wherein, E(·) represents the embedding function; The It is calculated using the following formula: The It is calculated using the following formula: Among them, the Indicates the mode in the second multimodal feature In local writing units The degree of local fluctuations.

4. The handwriting recognition method according to claim 2, characterized in that, The It is calculated using the following formula: Among them, the , and The learnable parameters of the machine learning model; Indicates the mode in the second multimodal feature In local writing units Local stability in; the Representing modes In local writing units The quality score in; Representing modes In local writing units The trainable discriminative response parameters of the machine learning model mentioned above; An index variable representing a modality, used to iterate through all modalities.

5. The handwriting recognition method according to claim 2, characterized in that, The recognition result is generated based on the final score. Based on global consistency score The above And temporal coherence score It is calculated using the following formula: Among them, the , and These are preset parameters or learnable parameters of the machine learning model, and .

6. The handwriting recognition method according to claim 1, characterized in that, The second multimodal feature includes a second macroscopic character shape feature, a second pen pressure distribution feature, and a second dynamic inversion feature.

7. The handwriting recognition method according to claim 6, characterized in that, The method further includes: Acquire multiple second images; wherein the second images include text written by the target author; Each of the second images is divided into multiple second local writing unit images; Based on all the second local writing unit images, the local stability and second multimodal features of the handwriting corresponding to each local writing unit are extracted respectively.

8. The handwriting recognition method according to claim 1, characterized in that, The local attention weights are used to construct the loss function of the machine learning model.

9. A computer device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the handwriting recognition method as described in any one of claims 1-8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the handwriting recognition method as described in any one of claims 1-8.

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