The RNN output sequence is mapped to a matrix of size 32×80. The popular Long Short-Term Memory (LSTM) implementation of RNNs is used, as it is able to propagate information through longer distances and provides more robust training-characteristics than vanilla RNN. RNN: the feature sequence contains 256 features per time-step, the RNN propagates relevant information through this sequence. While the image height is downsized by 2 in each layer, feature maps (channels) are added, so that the output feature map (or sequence) has a size of 32×256. Finally, a pooling layer summarizes image regions and outputs a downsized version of the input. Then, the non-linear RELU function is applied. First, the convolution operation, which applies a filter kernel of size 5×5 in the first two layers and 3×3 in the last three layers to the input. These layers are trained to extract relevant features from the image. OperationsĬNN: the input image is fed into the CNN layers. 1: The NN written as a mathematical function which maps an image M to a character sequence (c1, c2, …).
#IPAD TEXT RECOGNITION CODE CODE#
The first code we need to add in your app is setup of the camera object which performs the image capture. I won’t go into that here, but a guide to setting up React Native Firebase can be found here.
Your message to user when the microphone is accessed for theĪs under the hood this uses the Firebase library you’ll need a Firebase account setup for your app and the Firebase SDK configured in your app. The first time NSMicrophoneUsageDescription The first time NSPhotoLibraryUsageDescription Your message to user when the photo library is accessed for Your message to user when the camera is accessed for theįirst time NSPhotoLibraryAddUsageDescription Always try to ensure it’s clear why you’re requesting the permission or the user may well reject it and your app won’t work as intended. The framework will do the actual requesting, so we just need to set the message we’d like to display to the user, which is done using the OS plist permissions. yarn add react-native-cameraĪs we’re going to be accessing the phone’s hardware, we have to request permission to do so. pod ‘react-native-camera’, path: ‘./node_modules/react-native. In my case I wanted the TextDetector, so I had to add this. Thankfully this is as simple as adding a line to the Podfile. If you want to use any of the additional optional features beyond just taking photos and capturing videos, such as Face, Text or Barcode detection, then these need to be added separately. To add the iOS specific code we use Cocoapods. InstallationĪs with most React Native projects we add react-native-camera through your favoured package manager, in our case yarn.
#IPAD TEXT RECOGNITION CODE ANDROID#
I’m going to cover iOS here as that’s the platform I was interested in initially, but Android is also supported and is covered in all the relevant guides. Luckily there’s an open source project in React Native Community called react-native-camera, which wraps MLKit and gives you hooks into React Native to use many of the features.Īt the moment, this is using an older version of MLKit, but as it’s a widely supported community library I expect they’ll catch up soon, so it seemed a safe bet to use this library knowing that it would continue to be supported. However, sadly it doesn’t support React Native. Google provides a library called MLKit which can read text, recognise landmarks, track objects and many other pretty amazing things, so that seemed like the perfect starting point. In this case I was asked to investigate the feasibility of reading and recognising specific text in a React Native app. At Adapptor we’re sometimes tasked with creating prototypes for potential projects, which can be great fun as we’re frequently trying something that nobody else in the team has tried before.
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