Tensorflow is an open-source platform for machine learning. It is a deep learning framework, we use TensorFlow to build OCR systems for handwritten text, object detection, and number plate recognition. This solves accuracy issues. As a well-positioned AI development company, Oodles AI explores how to build and deploy handwritten text recognition using TensorFlow and CNN from scratch.
Handwritten Text Recognition (HTR) systems power computers to receive and interpret handwritten input from sources such as scanned images. The systems are able to convert handwritten texts into digital text or simply can digitize, store, and extract valuable information for accurate analysis. At Oodles, we use tools like OpenCV and provide TensorFlow development services to build a Neural Network (NN) which is trained on line-images from the off-line HTR dataset.
This Neural Network (NN) model split the text written in the scanned image into segmented line images. These line-images are smaller than images of the complete page image. 9/10 of the words of a segmented line from the validation-set are correctly recognized and the character error rate is around 8%.
The network is made up of 5 CNN and 2 RNN layers and workflow can be divided into 3 steps-
1. Create 5 Convolutional Neural Network (CNN ) layers
There are 5 CNN layers. First, the Convolutional layer with 5×5 filter kernels in the first 2 layers Second, the non-linear RELU function is there. Finally, a pooling layer. The output is a feature map.
2. Create a Recurrent neural network (RNN) layers and return its output
Create and stack two RNN layers with 256 units each and a bidirectional RNN from the stacked layers. Get 2 output sequences forward and backward of size 32×256. The output Calculates loss value and also decodes into the final text.
3. Create IAM-compatible dataset and train model
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Learn more: Handwritten Text Recognition Using Tensorflow and CNN
