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Ocr of rotated image #121
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Currently the recognition model and layout logic assumes that the image is approximately upright (some amount of rotation or skew is OK) and that the text is read left to right. To work with rotated or severely skewed images, they need to be rotated / de-skewed as a preprocessing step. Eventually this should be integrated into this library, but in the meantime you could try something like:
A more sophisticated approach would be to use an image classification model to infer the orientation of each word, or a sample of words. If a suitable model was created in eg. PyTorch and exported to ONNX, it could then be converted to RTen and used in the above preprocessing pipeline instead of heuristics. |
thanks for reply. |
I don't know. All the code that exists is in the ocrs-models repository, but for layout analysis that only includes some non-functional prototypes. In the meantime, if you happen to be working with documents that have a predictable layout, you can always substitute the |
exist layout analysis not working good for curve layout or complex image. i waiting for your layout analysis. |
hi, thanks for your useful ocr engine,
its works good but when i try to set rotated image it return bad result.
is there any fix tips?
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