# WordMetric¶

class mmocr.evaluation.metrics.WordMetric(mode='ignore_case_symbol', valid_symbol='[^A-Z^a-z^0-9^一-龥]', collect_device='cpu', prefix=None)[source]

Word metrics for text recognition task.

Parameters
• mode (str or list[str]) –

Options are: - ‘exact’: Accuracy at word level. - ‘ignore_case’: Accuracy at word level, ignoring letter

case.

• ’ignore_case_symbol’: Accuracy at word level, ignoring letter case and symbol. (Default metric for academic evaluation)

If mode is a list, then metrics in mode will be calculated separately. Defaults to ‘ignore_case_symbol’

• valid_symbol (str) – Valid characters. Defaults to ‘[^A-Z^a-z^0-9^一-龥]’

• collect_device (str) – Device name used for collecting results from different ranks during distributed training. Must be ‘cpu’ or ‘gpu’. Defaults to ‘cpu’.

• prefix (str, optional) – The prefix that will be added in the metric names to disambiguate homonymous metrics of different evaluators. If prefix is not provided in the argument, self.default_prefix will be used instead. Defaults to None.

Return type

None

compute_metrics(results)[source]

Compute the metrics from processed results.

Parameters

results (list[Dict]) – The processed results of each batch.

Returns

The computed metrics. The keys are the names of the metrics, and the values are corresponding results.

Return type

Dict

process(data_batch, data_samples)[source]

Process one batch of data_samples. The processed results should be stored in self.results, which will be used to compute the metrics when all batches have been processed.

Parameters
• data_batch (Sequence[Dict]) – A batch of gts.

• data_samples (Sequence[Dict]) – A batch of outputs from the model.

Return type

None