The following is a JSON data containing the evaluation result from OCR detection model's inference on 100 testing images. It includes information on font gray color, background, font size, font class, recognition score distribution, and recognition error distance distribution.
{'font_gray_color': {30: 1.0, 51: 1.0, 72: 1.0, 93: 0.938, 114: 0.875,135：1.0，156： 1.0， 177：1.0，198:1.0}，background': {'OriginalBG': 0.969,'white': 0.979}, 'font_size': {30: 1.0, 31: 0.969, 32: 0.875},'font_class': ('arial.ttf': 0.979,'simsun.ttc': 0.969},'rec_score_distribute': {'0.95': 2, '0.9': 1, '0.85': 2,
'0.8': 2,'0.75': 0, '0.7': 1,'0.65': 0,'0.6': 0, '0.55': 0, '0.5': 0, '0.5-': 0),'rec_err _distance_distribute': {'Dist:0': 8,
'Dist: 1': 2,'Dist: 2': 0,'Dist:3': 0, 'Dist:4': 0,'Dist:5': 0, 'Dist:5+': 0}
Your task is to analyze this data and identify any patterns or trends in the model's performance across different feature levels. Specifically, assess the font gray color, background, font size, and font class. Additionally, identify the score ranges where the OCR model performs well and where it struggles.