The json data below is the evaluation result obtained from OCR detection model inference on 100 testing images. {'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}
I need you to:
1.Assess the font gray color, background, font size, font class and identify any patterns or trends in the model's performance across different feature levels.
2. Identify the score ranges whereOCR model performs well and where it struggles.
3. Provide targeted suggestions for improving the model's performance in handling above various features.
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