Patent ID: 11881038
Assignee: KIA CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A system of multi-directional scene text recognition based on multi-element attention mechanism, comprising:
a feature extractor performing normalization processing for a text row/column image I output from an external text detection module by a feature extractor, extracting a feature for the normalized image by using a deep convolutional neural network to acquire an initial feature map F0, and adding a 2-dimensional directional positional encoding P to the initial feature map F0 in order to output a multi-channel feature map F, wherein a size of the feature map F is HF×WF, and the number of channels is D;
an encoder modeling each element of the feature map F output from the feature extractor as a vertex of a graph, and implementing multi-element attention mechanisms of local, neighboring, and global through designing three adjacency matrices for the graph including a local adjacency matrix, a neighboring adjacency matrix, and a global adjacency matrix to convert the multi-channel feature map F into a hidden representation H; and
a decoder converting the hidden representation H output from the encoder into recognized text and setting the recognized text as the output result,
wherein the encoder,
is constituted by two identical encoding units, and each encoding unit includes a local attention module MEALocal, a neighboring attention module MEANeighbor, and a global attention module MEAGlobal, and a feed forward network module, and,
decomposes the elements of the multi-channel feature map F to a vector according to column order, and obtains a feature matrix X, and the dimension of the feature matrix X is N×D, i.e., an N-row and D-column matrix, the N represents the number of elements of each channel, N=HF×WF, and D represents the number of channels of the feature map F.