annotator | Implements a simple , fast and useful annotation support | Build Tool library

The offline model of T5 - t5_base_en_2.7.1_2.4_1610133506835 - was trained on SparkNLP 2.7.1, and there was a breaking change in 2.7.2.

Solved by downloading and re-saving the new version with

I can't see what's wrong off hand, but it seems like something with the coordinates systems since the lower-right corner of the box is out of frame, or maybe the resizing. I'd start by looking at these values - can you print these out:

• CAMERA_WIDTH, CAMERA_HEIGHT
• input_height, input_width
• ymin, xmin, ymax, xmax
• start_point #(xmin,ymin)
• end_point #(xmax,ymax)

And then display the model input image:

• image = Image.open(stream).convert('RGB').resize( (input_width, input_height), Image.ANTIALIAS)

And then display the image you are using to draw the bounding box:

• open_cv_image

I believe this issue after discussion is the spans were computed from Unicode strings that used surrogate pairs for Unicode code points > U+FFFF. Python 2 and other languages like Java and C# store Unicode strings with UTF-16 code units instead of abstracted code points like Python 3. If I treat the test data as UTF-16LE-encoded, the answer comes out:

You can use the Gini index as a metric, and then do a Grid Search based on this metric. Tell me if you have any other question.

One way of doing this in Flink might be to use a KeyedProcessFunction, i.e. a function that can:

• process each event in your stream
• maintain some state
• trigger some logic with a timer based on event time

So it would go something like this:

• you need to know some kind of "max out of orderness" about your data. Based on your description, let's assume 100ms for example, such that when processing data at timestamp 1612103771212 you decide to consider you're sure to have received all data until 1612103771112.
• your first step is to keyBy() your stream, keying by particle id. This means that the logic of next operators in your Flink application can now be expressed in terms of a sequence of events of just one particle, and each particle is processed in this manner in parallel.

Something like this:

The coref chains have a sentenceIndex and a beginIndex which should correlate to the position in the sentence. You can use this to correlate the two.

https://github.com/stanfordnlp/stanza/blob/f0338f891a03e242c7e11e440dec6e191d54ab77/doc/CoreNLP.proto#L319

Edit: quick and dirty change to your example code:

I have reconstructed some of your code to achieve the answer. Please be aware that I've switched things from Jquery to JS.

New HTML Page:

1. If you want to change the ratio of the image before the detection is done you can use the CropHintsParams in the image context to build an AnnotateImageRequest instead of passing the image directly.

2. If you want to apply a bounding box before the detection I would suggest performing a handmade image crop, because currently I don't see any available option through the C# client library. Check this thread. Otherwise you can filter the results afterwards with the BoundingPoly field in the text annotation result.

I would take a look at the REST reference to understand how the request should be build.

You can also take a look at the Try it! page and check how the JSONs are built.

In order to get better results, it is recommended not to use lossy formats (example of lossy format: JPEG). Using or reducing file sizes for such lossy formats may result in a degradation of image quality, and hence, Vision API accuracy.

The image’s recommended size is 1024 x 768 for the features TEXT_DETECTION and DOCUMENT_TEXT_DETECTION. As an additional note:

The Vision API requires images to be a sufficient size so that important features within the request can be easily distinguished. Sizes smaller or larger than these recommended sizes may work. However, smaller sizes may result in lower accuracy, while larger sizes may increase processing time and bandwidth usage without providing comparable benefits in accuracy. Image size should not exceed 75M pixels (length x width) for OCR analysis.

The items discussed above can be found in this article.

With the code you are using, you can alternately use the DOCUMENT_TEXT_DETECTION feature and select the ones which gives you better results. I see that you are using the code in this link for TEXT_DETECTION. Try using the code in this link for DOCUMENT_TEXT_DETECTION.

In case that issue still persists after the suggested actions, I recommend that you contact Google Cloud Platform Support or create a public issue via this link.