text-spotting-0002-recognizer-decoder

Use Case and High-Level Description

This is a text spotting model that simultaneously detects and recognizes text. The model detects symbol sequences separated by space and performs recognition without a dictionary. The model is built on top of the Mask-RCNN framework with additional attention-based text recognition head.

Symbols set is alphanumeric: 0123456789abcdefghijklmnopqrstuvwxyz.

This model is 2D attention-based GRU decoder of text recognition head.

Example

text-spotting-0002.png

Specification

Metric Value
Word spotting hmean ICDAR2015, without a dictionary 59.04%
GFlops 0.002
MParams 0.273
Source framework PyTorch*

Hmean Word spotting is defined and measured according to the Incidental Scene Text (ICDAR2015) challenge.

Performance

Inputs

  1. Name: encoder_outputs , shape: [1x(28*28)x256]. Encoded text recognition features.
  2. Name: prev_symbol , shape: [1x1]. Index in alphabet of the previously generated symbol.
  3. Name: prev_hidden, shape: [1x1x256]. Previous hidden state of GRU.

Outputs

  1. Name: output, shape: [1x38]. Encoded text recognition features.
  2. Name: hidden, shape: [1x1x256]. Current hidden state of GRU.

Legal Information

[*] Other names and brands may be claimed as the property of others.