Which Object detection model will give the best result on images when the speed is not a problem for Text Images

Note that there are two problems in this case: segmentation and classification. A neural net might be a solution for both steps in this case because you can easily generate zillions of labelled test images. Nevertheless, a classic approach should yield comparable results with much less efforts:

1. Use a simple page segmentation alorithm like runlength smearing or bounding box merging for segmenting the image into regions
2. Classify each region with an arbitrary classifier. You can use a NN on all normalized input pixels for this, but other classifiers like kNN should also work with gradient histograms as features (the gradients are computed on quasi grayscale images, which are generated from the onbit images by blurring). Gradient histograms were the state-of-the art features before the renaissance of neural nets.

Out of curiosity, I have tried out step one with the python library Gamera (gamera.sf.net) with the following code:

from gamera.core import *
init_gamera()

img = load_image("MathExpressionInputExample.png")
img = img.to_onebit()

img.remove_border()
segments = img.runlength_smearing()

# now you could process each segment (e.g. saving it to a file)
for seg in segments:
    # do some stuff

# visualize the result
color_ccs = img.graph_color_ccs(segments)
color_ccs.save_PNG("segments.png")

The result looks reasonable to me(note that the colors only indicate the segmentation, with adjacent segments having different colors):