If you're trying to handle text "in the wild" and not scanned documents, the keyword is "scene text". Most papers are focused on either detection/localization,

If you're trying to handle text "in the wild" and not scanned documents, the keyword is "scene text". Most papers are focused on either detection/localization, i.e. finding the location of text, or recognition, i.e. recognizing the actual content given a cropped text image. Here are some current state-of-the-art papers + code where available about detection: Fused Text Segmentation Networks for Multi-oriented Scene Text Detection https://arxiv.org/abs/1709.03272 https://arxiv.org/abs/1709.03272 EAST: An Efficient and Accurate Scene Text Detector https://arxiv.org/abs/1704.03155 https://arxiv.org/abs/1704.03155 https://github.com/argman/EAST https://github.com/argman/EAST Detecting Oriented Text in Natural Images by Linking Segments https://arxiv.org/abs/1703.06520 https://arxiv.org/abs/1703.06520 https://github.com/dengdan/seglink https://github.com/dengdan/seglink Arbitrary-Oriented Scene Text Detection via Rotation Proposals https://arxiv.org/abs/1703.01086 https://arxiv.org/abs/1703.01086 https://github.com/mjq11302010044/RRPN https://github.com/mjq11302010044/RRPN And for recognition: An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition https://arxiv.org/abs/1507.05717 https://arxiv.org/abs/1507.05717 https://github.com/bgshih/crnn https://github.com/bgshih/crnn Robust Scene Text Recognition with Automatic Rectification https://arxiv.org/abs/1603.03915 https://arxiv.org/abs/1603.03915