#Text recognition software korean license
#Text recognition software korean plus
#Text recognition software korean free

Goodfellow, I.J., Bulatov, J., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks. Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition.

In: 10th International Conference on Knowledge and Systems Engineering, KSE (2018) Nguyen, T., Nguyen, D.: A new convolutional architecture for Vietnamese car plate recognition. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. In: International Conference on Education Technology and Computer (2009) Qadri, M.T., Asif, M.: Automatic number plate recognition system for vehicle identification using optical character recognition.

CRNN вЂ“ Convolutional Recurrent Neural Networks.

Nevertheless, in this research, we developed our own method that is a combination of scene text recognition technique with Geometrical Image Transformation (GIT) to recognize number plates for combined neural networks and achieving 99.8% and 95.7% of detection and recognition accuracy respectively.

#Text recognition software korean license

Traditional approaches on license plate detection have achieved quite a high accuracy in detection and recognition, in which mostly Optical Character Recognition (OCR) is used. Despite, there have been many approaches and studies on license plate localization, character segmentation and recognition, there have not been highly demanded results particularly using deep neural networks. It is an advanced application that targets to provide deep learning solution that can be applied in many areas including Intelligent Transportation System, Internet of Things and Smart City. If you are interested in the topic I suggest you also look at Text recognition (OCR) with Tesseract and OpenCV.We developed a deep learning application to detect and recognize Korean carsвЂ™ license plates from images. Text_detected = parsed_results.get('ParsedText') Now from the result letвЂ™s extract only the text as it is our focus on this project: parsed_results = result.get('ParsedResults') The other values depend from the post parameters we did set before.ДЇor example if we enabled the text Overlay we would get the coordinates with the position of the text in the image.

#Text recognition software korean plus

Result contains the text read from the OCR engine plus a few other values. WeвЂ™re going first of all to extract the content of result, then we convert the content into a dictionary. The function is going to send the image to the server and in return weвЂ™re going to get the response from the server. Go on this page to see all the вЂњPOST parametersвЂќ you can use. We need to insert the api key where now itвЂ™s written вЂњYOURAPIKEYHEREвЂќ, and language is the language of our text.

And then вЂњDataвЂќ which contains the post parameters of the OCR engine.

Called вЂњFilesвЂќ which contains the name of the file and the file bytes we generated before after we compressed the image.

Later we send the bytes to the server using the python library requests. _, compressedimage = cv2.imencode('.jpg', roi, ) It must be converted to bytes to be sent to the server. On line 16 we convert the image into bytes.

#Text recognition software korean free

The simple reason for this compression is that using the free service we can send image with maximum 1mb of size, so this compression will shrink the size of our image. On line 15 weвЂ™re going to compress the image in JPG format. On line 14 we have the url of ocr.space api where we need to send our image.

We now have the image and our goal is to send the image to the orc.space server in order to be processed.