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 a Mask-RCNN-based text detector with ResNet50 backbone and additional text features output.




Metric Value
Word spotting hmean ICDAR2015, without a dictionary 61.01%
GFlops 185.169
MParams 26.497
Source framework PyTorch*

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



  1. Name: im_data , shape: [1x3x768x1280]. An input image in the [1xCxHxW] format. The expected channel order is BGR.
  2. Name: im_info, shape: [1x3]. Image information: processed image height, processed image width and processed image scale with respect to the original image resolution.


  1. Name: classes, shape: [100]. Contiguous integer class ID for every detected object, 0 for background (no object detected).
  1. Name: scores, shape: [100]. Detection confidence scores in the [0, 1] range for every object.
  1. Name: boxes, shape: [100x4]. Bounding boxes around every detected object in the (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
  1. Name: raw_masks, shape: [100x2x28x28]. Segmentation heatmaps for all classes for every output bounding box.
  1. Name: text_features, shape [100x64x28x28]. Text features that are fed to a text recognition head.

Legal Information

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