If you are looking for a ready-to-use production-grade OCR
#GAMEBRYO ENGINE CHARACTER MODEL HOW TO#
So we have chosen 3 Google product logos only to demonstrate how to do OCR with Taken by a smartphone camera in a low lighting condition). The models are not general enough for OCR in the wild (say, random images Is trained using synthetic data with English letters and numbers, so only The mapping indices to the alphabet list '0123456789abcdefghijklmnopqrstuvwxyz' Limitations The text recognition model returns a 2-D float32 Tensor of shape (1, 48) as The text detection model returns a 4-D float32 Tensor of shape (1, 80, 80, 5)Īs bounding box and a 4-D float32 Tensor of shape (1,80, 80, 5) as detection The text recognition model accepts a 4-D float32 Tensor of (1, 31, 200, 1) as The text detection model accepts a 4-D float32 Tensor of (1, 320, 320, 3) as ** this model could not use GPU delegate since we need TensorFlow ops to run it Inputs Performance benchmark numbers are generated with the tool described In our case,īoth models are from TensorFlow Hub and they are FP16 quantized models. Processed bounding boxes into a text recognition model to determine specificĬharacters inside the bounding boxes (we also need to do Non-Maximal Supression, Model to detect the bounding boxes around possible texts.
OCR tasks are often broken down into 2 stages. If you are using a platform other than Android, or you are already familiar with If you are new to TensorFlow Lite and are working with Android, we recommendĮxploring the following example application that can help you get started. It uses a combinationĪs an OCR pipeline to recognize text characters. Reference app demos how to use TensorFlow Lite to do OCR. Optical character recognition (OCR) is the process of recognizing charactersįrom images using computer vision and machine learning techniques.
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