Residual context refinement network architecture for optical character recognition

A computer-implemented method comprising:

• receiving a request to recognize text in an image;
• extracting features from the image and generating a visual feature sequence from the extracted features;
• performing selective contextual refinement at least one time to generate a text prediction by:
• generating a contextual feature map and combining the contextual feature map with the visual feature sequence into a visual feature space, and
• applying a selective decoder that utilizes a two-step attention on the visual feature space to generate a text prediction, wherein the two-step attention includes performing a 1-D self-attention computation to generate attentional features and decoding the attentional features to generate the text prediction;
• and
• outputting the generated text prediction.

A system comprising:

• a first one or more electronic devices to store an image in a multi-tenant provider network;
• and
• a second one or more electronic devices to implement an image text recognizing service in the multi-tenant provider network, the image text recognizing service including instructions that, upon execution, cause the image text recognizing service to:
• receive a request to recognize text in a stored image,
• extract features from the image and generating a visual feature sequence from the extracted features, and
• perform selective contextual refinement at least one time to generate a text prediction by:
• generating a contextual feature map and combining the contextual feature map with the visual feature sequence into a visual feature space, and
• applying a selective decoder that utilizes a two-step attention on the visual feature space to generate a text prediction, wherein the two-step attention includes performing a 1-D self-attention computation to generate attentional features and decoding the attentional features to generate the text prediction;
• and
• output the generated text prediction.