# handwritten-english-recognition-0001¶

## Use Case and High-Level Description¶

This is a network for handwritten English text recognition scenario. It consists of a CNN followed by Bi-LSTM, reshape layer and a fully connected layer. The network is able to recognize English text consisting of characters in the GNHK dataset.

## Example¶

-> ‘Picture ID. and Passport photo’

## Specification¶

Metric

Value

GFlops

1.3182

MParams

0.1413

Accuracy on GNHK test subset (excluding images wider than 2000px after resized to height 96px with aspect ratio)

82.0%

Source framework

PyTorch*

Note: to achieve the accuracy, images from the GNHK test set should be binarized using adaptive thresholding, and preprocessed into single-line text images, using the coordinates from the accompanying JSON annotation files in the GNHK dataset. See <omz_dir>/models/intel/handwritten-english-recognition-0001/preprocess_gnhk.py.

This model adopts label error rate as the metric for accuracy.

## Inputs¶

Grayscale image, name - actual_input, shape - 1, 1, 96, 2000, format is B, C, H, W, where:

• B - batch size

• C - number of channels

• H - image height

• W - image width

NOTE: the source image should be resized to specific height (such as 96) while keeping aspect ratio, and the width after resizing should be no larger than 2000 and then the width should be right-bottom padded to 2000 with edge values.

## Outputs¶

Name - output, shape - 250, 1, 95, format is W, B, L, where:

• W - output sequence length

• B - batch size

• L - confidence distribution across the supported symbols in GNHK

The network output can be decoded by CTC Greedy Decoder.

The network also outputs 10 LSTM hidden states of shape 2, 1, 256, which can be simply ignored.

## Demo usage¶

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: