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The second stage involves running a recognition algorithm on the bounding boxes generated by your localisation algorithm, you could use tesseract here and get some decent results however if there is a lot of irregular text (curved/distorted) then I would recommend using https://github.com/clovaai/deep-text-recognition-benchmark they provide some good pretrained models that you could then finetune.
This is a 2 stage problem, first you need to localise text in the image and then recognise it. For localisation you can use a https://github.com/facebookresearch/detectron2 pretrained Faster-RCNN model and then fine tune it on an OCR dataset such as https://vision.cornell.edu/se3/coco-text-2/ (though you can probably find a different dataset that more closely matches your use case).