Research into the topic of handwritten character recognition spans several disciplines, including AI, CV (Computer Vision), and pattern recognition. A handwriting recognition algorithm can take data from cameras and touch screens and turn it into a format that a computer can understand.
What is Handwriting Recognition?
Large volumes of handwritten data cannot be accurately transcribed without using a more laborious and error-prone manual technique. The time spent transcribing huge amounts of text may be greatly reduced by using an automated handwriting recognition system, which can also serve as a foundation for the creation of new machine learning applications.
A computer or device with handwriting recognition software may read handwriting from paper documents, photographs, or other devices and convert it into text, or it can receive handwritten input directly through a touchscreen.
Libraries and Datasets Widely Used for Handwriting Recognition
The MNIST database is a massive library of handwritten digits used to train different image processing algorithms. There are more than 60,000 photos used for training in the MNIST database, and another 10,000 are used for testing.
Kaggle A-Z Database
Kaggle is a web-based data-science competition where users may discover and submit data sets, as well as explore and construct models.
Each handwritten alphabet in the 2828-pixel-tall photos included in the Kaggle A-Z database is centered in a 2020-pixel-tall box. The dataset is organized into 26 folders (A-Z). Grayscale images are used to store all images.
OpenCV is a library that was developed to standardize the underlying infrastructure for computer vision applications and hasten the incorporation of machine perception into consumer goods. Image processing and handwriting recognition are two of their most common applications.
TensorFlow is an open-source library that may be used for AI and machine learning at no cost. It's useful for many things, but deep neural network training and inference are where it really shines.
Keras is a high-level API based on the framework of TensorFlow, providing a user-friendly and highly productive means of addressing a wide range of machine learning issues, with a special emphasis on cutting-edge deep learning techniques. It gives you the key abstractions and building pieces for rapidly prototyping, developing, and releasing machine learning solutions.
What is Handwriting Synthesis?
Automatically producing data that looks like a real human being’s handwriting is called "handwriting synthesis." To that end, researchers have developed this technique that uses a computer to make text that looks and feels very similar to the user's own handwriting. Handwriting synthesis has several potential uses, including the enhancement of text recognition systems, the customization of fonts, the identification and propagation of writers, and more.
How is the Missing Data Synthesized?
To begin with, a controllable model is created for the generation of sample handwriting. This model is created by sifting through a distribution of handwriting examples. This allows for the creation of sample handwritten characters to be used for the missing data. This is passed through a handwriting recognizer.
Next, the optimization of the synthesized sample data is carried out. Models are created to optimize the datasets(both the real and the synthesized datasets) by splitting them into training, validation and testing sets. Character Error Rate (CER) is used to compare the performance of different models on actual and simulated data.
Furthermore, the optimal model(s) with the best accuracy is trained more using a careful mix of artificial and actual data. Finally, these models are tested on actual handwriting recognition datasets to synthesize the missing data.
Despite the growing interest in handwriting synthesis, there is yet to be a comprehensive overview of the field. Researchers have been attempting to do this for quite some time, and all we have done is illuminate the typical procedures they use, in order to synthesize the missing data. You can visit Deep Learning extends the power of machine vision to know more about how AI is used for recognition through images.