fix API usage examples #260
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| api.SetRectangle(box['x'], box['y'], box['w'], box['h']) | ||
| ocrResult = api.GetUTF8Text() |
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Note: it's not enirely wrong to use the API this way, because GetComponentImages makes copies of the segment images and bboxes, so it does not hurt that SetRectangle invalidates the layout analysis results. But it is still not useful to loop that way if the ultimate goal is the text – you would rather look into the iterator directly for the text. Also, in this formulation, you would have needed to at least set the PSM to line level for a decent OCR result.
| Orientation and script detection (OSD): | ||
| ``````````````````````````````````````` |
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Unfortunately, the differ from here on compares the wrong lines. I did not replace the OSD example, but inserted a full GetIterator example (i.e. the second half of the above loop), and conflated the two OSD variants below into one.
| bbox = {'x': int(bbox[0]), | ||
| 'y': int(bbox[1]), | ||
| 'w': int(bbox[2])-int(bbox[0]), | ||
| 'h': int(bbox[3])-int(bbox[1])} |
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It's noteworthy that PageIterator.BoundingBox gives a completely different format than GetComponentImages/GetRegions – better be explicit here.
| print("Deskew angle: {:.4f}".format(deskew_angle)) | ||
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| or more simply with ``OSD_ONLY`` page segmentation mode: | ||
| Layout analysis with orientation and deskewing: |
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It's important to understand that there are two distinct mechanisms providing orientation detection: the normal page layout analysis (which you can use with any model, including LSTMs) and the dedicated osd model (which is legacy-only). It was documented the other way round.
| print("Orientation: {}".format(membername(Orientation, orientation))) | ||
| print("WritingDirection: {}".format(membername(WritingDirection, direction))) | ||
| print("TextlineOrder: {:d}".format(membername(TextlineOrder, order))) | ||
| print("Deskew angle: {:.1f}°".format(deskew_angle * 180 / math.pi)) |
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Seems more informative to me to get left-to-right or PAGE_UP strings instead of just 0 as output.
Also converting the angle from radians to degrees is more illustrative.
| with PyTessBaseAPI(psm=PSM.OSD_ONLY, oem=OEM.LSTM_ONLY) as api: | ||
| with PyTessBaseAPI(psm=PSM.OSD_ONLY, | ||
| oem=OEM.TESSERACT_ONLY, | ||
| lang="osd") as api: |
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Believe me, it does not work with LSTMs. The standalone CLI even loads the osd model anyway if the user forgot to. On the API, it will look like it works without loading it, because the default model eng will get loaded. But there are only symbols from one script in that model, Latin, thus no actual script detection would happen. (The "signal" would always be strong, because no competing scripts are loaded, like in osd. DetectOS / os_detect is very special, because it does not use multiple models, but needs a single model with multiple scripts – for which osd is of course the most versatile, but also frk contains both Latin and Fraktur, hin contains some Latin as well etc.)
| @@ -246,24 +270,25 @@ Iterator over the classifier choices for a single symbol: | |||
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| with PyTessBaseAPI() as api: | |||
| api.SetImageFile('/usr/src/tesseract/testing/phototest.tif') | |||
| api.SetVariable("save_blob_choices", "T") | |||
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That is long gone. I am not sure what is needed for legacy models today. But for LSTMs, it's lstm_choice_mode. Unfortunately, it usually does not yield any actual results. It's complicated...
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