anls 0.0.2

ANLS: Average Normalized Levenshtein Similarity

ANLS: Average Normalized Levenshtein Similarity

This python script is based on the one provided by the Robust Reading Competition for evaluation of the InfographicVQA task.

The ANLS metric

The Average Normalized Levenshtein Similarity (ANLS) proposed by [Biten+ ICCV'19] smoothly captures the OCR mistakes applying a slight penalization in case of correct intended responses, but badly recognized. It also makes use of a threshold of value 0.5 that dictates whether the output of the metric will be the ANLS if its value is equal or bigger than 0.5 or 0 otherwise. The key point of this threshold is to determine if the answer has been correctly selected but not properly recognized, or on the contrary, the output is a wrong text selected from the options and given as an answer.

More formally, the ANLS between the net output and the ground truth answers is given by equation 1. Where $N$ is the total number of questions, $M$ total number of GT answers per question, $a_{ij}$ the ground truth answers where $i = {0, ..., N}$, and $j = {0, ..., M}$, and $o_{qi}$ be the network's answer for the ith question $q_i$:

$$ \mathrm{ANLS} = \frac{1}{N} \sum_{i=0}^{N} \left(\max_{j} s(a_{ij}, o_{qi}) \right), $$

where $s(\cdot, \cdot)$ is defined as follows:

$$ s(a_{ij}, o_{qi}) = \begin{cases} 1 - \mathrm{NL}(a_{ij}, o_{qi}), & \text{if } \mathrm{NL}(a_{ij}, o_{qi}) \lt \tau \ 0, & \text{if } \mathrm{NL}(a_{ij}, o_{qi}) \ge \tau \end{cases} $$

The ANLS metric is not case sensitive, but space sensitive. For example:

Q: What soft drink company name is on the red disk?

Possible answers:

• $a_{i1}$ : Coca Cola
• $a_{i2}$ : Coca Cola Company

Net output ($o_{qi}$)	$s(a_{ij}, o_{qi})$	Score (ANLS)
The Coca	$a_{i1} = 0.44$, $a_{i2} = 0.29$	0.00
CocaCola	$a_{i1} = 0.89$, $a_{i2} = 0.47$	0.89
Coca cola	$a_{i1} = 1.00$, $a_{i2} = 0.53$	1.00
Cola	$a_{i1} = 0.44$, $a_{i2} = 0.23$	0.00
Cat	$a_{i1} = 0.22$, $a_{i2} = 0.12$	0.00

Installation

• From pypi

pip install anls

• From GitHub

pip install git+https://github.com/shunk031/ANLS

How to use

From CLI

calculate-anls \
    --gold-label-file test_fixtures/evaluation/evaluate_json/gold_label.json \
    --submission-file test_fixtures/evaluation/evaluate_json/submission.json \
    --anls-threshold 0.5

❯❯❯ calculate-anls --help
usage: calculate-anls [-h] --gold-label-file GOLD_LABEL_FILE --submission-file SUBMISSION_FILE [--anls-threshold ANLS_THRESHOLD]

Evaluation command using ANLS

optional arguments:
  -h, --help            show this help message and exit
  --gold-label-file GOLD_LABEL_FILE
                        Path of the Ground Truth file.
  --submission-file SUBMISSION_FILE
                        Path of your method's results file.
  --anls-threshold ANLS_THRESHOLD
                        ANLS threshold to use (See Scene-Text VQA paper for more info.).

From python script

>>> from anls import anls_score
>>> ai1 = "Coca Cola"
>>> ai2 = "Coca Cola Company"
>>> net_output = "The Coca"
>>> anls_score(prediction=net_output, gold_labels=[ai1, ai2], threshold=0.5)
0.00
>>> net_output  = "CocaCola"
>>> anls_score(prediction=net_output, gold_labels=[ai1, ai2], threshold=0.5)
0.89
>>> net_output  = "Coca cola"
>>> anls_score(prediction=net_output, gold_labels=[ai1, ai2], threshold=0.5)
1.0

References

• Biten, Ali Furkan, et al. "Scene text visual question answering." Proceedings of the IEEE/CVF international conference on computer vision. 2019.