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from ...smp import *
def OCRBench_eval(eval_file):
OCRBench_score = {
'Regular Text Recognition': 0,
'Irregular Text Recognition': 0,
'Artistic Text Recognition': 0,
'Handwriting Recognition': 0,
'Digit String Recognition': 0,
'Non-Semantic Text Recognition': 0,
'Scene Text-centric VQA': 0,
'Doc-oriented VQA': 0,
'Key Information Extraction': 0,
'Handwritten Mathematical Expression Recognition': 0
}
logger = get_logger('Evaluation')
data = load(eval_file)
lt = len(data)
lines = [data.iloc[i] for i in range(lt)]
for i in tqdm(range(len(lines))):
line = lines[i]
predict = str(line['prediction'])
answers = eval(line['answer'])
category = line['category']
if category == 'Handwritten Mathematical Expression Recognition':
for j in range(len(answers)):
answer = answers[j].strip().replace('\n', ' ').replace(' ', '')
predict = predict.strip().replace('\n', ' ').replace(' ', '')
if answer in predict:
OCRBench_score[category] += 1
break
else:
for j in range(len(answers)):
answer = answers[j].lower().strip().replace('\n', ' ')
predict = predict.lower().strip().replace('\n', ' ')
if answer in predict:
OCRBench_score[category] += 1
break
final_score_dict = {}
final_score_dict['Text Recognition'] = (
OCRBench_score['Regular Text Recognition'] + OCRBench_score['Irregular Text Recognition']
+ OCRBench_score['Artistic Text Recognition'] + OCRBench_score['Handwriting Recognition']
+ OCRBench_score['Digit String Recognition'] + OCRBench_score['Non-Semantic Text Recognition']
)
final_score_dict['Scene Text-centric VQA'] = OCRBench_score['Scene Text-centric VQA']
final_score_dict['Doc-oriented VQA'] = OCRBench_score['Doc-oriented VQA']
final_score_dict['Key Information Extraction'] = OCRBench_score['Key Information Extraction']
final_score_dict['Handwritten Mathematical Expression Recognition'] = \
OCRBench_score['Handwritten Mathematical Expression Recognition']
final_score_dict['Final Score'] = (
final_score_dict['Text Recognition'] + final_score_dict['Scene Text-centric VQA']
+ final_score_dict['Doc-oriented VQA'] + final_score_dict['Key Information Extraction']
+ final_score_dict['Handwritten Mathematical Expression Recognition']
)
final_score_dict['Final Score Norm'] = float(final_score_dict['Final Score']) / 10
score_pth = eval_file.replace('.xlsx', '_score.json')
dump(final_score_dict, score_pth)
logger.info(f'OCRBench_eval successfully finished evaluating {eval_file}, results saved in {score_pth}')
logger.info('Score: ')
for key, value in final_score_dict.items():
logger.info('{}:{}'.format(key, value))
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