This is the result on the performance of a text recognition model for a given input image and its 100 variants. The font_gray_color indicates the brightness value of the text and its corresponding accuracy. The background class represents the background of the input image and its corresponding accuracy. The font_size represents the font size of the text and its corresponding accuracy. Finally, the font_class represents the font type of the text and its corresponding accuracy.
{'font _gray_color': [{30: 0.8125, 31: 1.0,32: 0.95, 33: 0.875,34:0.42,35: 0.95, 36:0.91, 37: 0.96，38: 0.67, 39: 0.375,40: 0.72,41: 0.75,42: 0.92, 43: 0.95, 44: 0.625, 45: 0.875,46: 0.98, 47: 0.375,48: 0.625, 49: 0.5,50: 0.25, 51: 0.92, 52: 1.0, 53: 0.66,54:0.58, 55: 0.83,56: 0.84, 57: 0.625, 58:0.96, 59: 0.96, 60:1.0}],
background': ['OriginalBG': 1.0,'salt': 0.37, scratch': 0.91,: 0.67,'white': 1.0}], 
'font_size': [{11: 0.36, 12: 0.825,13:0.6125,14:0.74,15:0.6625,16: 0.975, 17: 0.8875,18:0.9125, 19: 0.8875,20:0.86,21: 1.0}],
"font class': ['arial.ttf'0.75, 'simsun.ttc: 0.78}]}
 can you give me some advices to improve the performance of this text recognition model based on the information I gave to you?