Deep learning, a part of Machine Learning, is widely used in handwriting recognition. The task of manual transcribing is an arduous process that ought to have errors. Automatic handwriting recognition cuts down the time required for transcribing large amounts of text and acts as a framework for machine learning application development. Handwriting recognition is an ongoing field of research surrounding computer vision, artificial intelligence, and pattern recognition. The handwriting recognition algorithm acquires and monitors characteristics from touch-screen devices and pictures and converts them to a machine-readable form.
Significant processes right up to human-competitive performance from classical performances have been possible because of extensive research. This article is an overview of that journey and the potential that the field holds.
Learning of the Blog
- Recognition Techniques
- Optical Character Recognition
- Character Recognition Algorithm
- Neural Networks
- CapsNets- The future
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Handwritten recognition systems are major of two types- online and offline. To learn progressively, based on the user’s feedback, both types can be implemented in applications while performing offline learning on data in parallel. Structural, statistical, and syntactic methods, along with neural networks, are used for offline and online handwriting recognition fields. Some recognition systems apply a single character or word wise recognition while others identify strokes. Text is analyzed after being written in offline handwriting recognition. Only the binary output of a character can be analyzed against a background. Using a digital stylus provides more information such as pressure, stroke, and speed of writing. However, in the case of archives, historical documents, and hand-filled forms, offline methods is still a necessity.
We look at what was there before Deep Learning, what is the present technology, and what is the future of handwriting recognition as believed by machine learning experts worldwide.
Optical Character Recognition
Optical Character Recognition or OCR recognizes text inside images and scanned documents. Any image containing written text can be virtually converted to machine-readable text data. It became popular in the early 1990s in an attempt to digitize newspapers and was earlier used for postal mail classification. Since then, there have been many improvements, and now solutions deliver perfect OCR accuracy. Zonal OCR is more advanced and can automate complex document-based workflows. The advantages of OCR software are considerable. The two substantial limitations are Character and feature extraction. Cursive writing and words without spaces pose evaluation issues. Individual properties of symbols were hardcoded, making them less flexible. OCR is a hidden technology that powers public services and systems particularly implied in document indexing, data entry automation, number plate recognition, etc.
Character Recognition Algorithm
There are three main categories of character recognition, and they are used in sequence typically. While feature extraction is necessary for correct classification, pre-processing helps in making feature extraction a smoother process. Image pre-processing includes image segmentation, noise removal, scaling, and cropping for accurate character prediction. The system first accepts a scanned image in JPG or BMT format. It is essential to remove as much noise as possible because noise introduced by digital capturing and image conversion makes it hard to identify parts of the object of interest. In segmenting, a sequence of characters is segmented into a sub-image of an individual character resized into 30×20 pixels. Feature extraction means identifying relevant features that can discriminate against independent instances. Classification and recognition stage is the decision making stage of the recognition system.
A neural network can learn features by dataset analysis and classification of unseen images based on weights. When the kernel is passed over the image, certain features are extracted in the convolutional layers. To make all classifications, multiple kernels learn all the features within a dataset. Neural networks solve the issue of feature extraction in OCR methods. No manual hardcoding is required in the case of neural networks, as the training process is learned as parameters. This alleviates the challenges in feature extraction in classical methods and makes deep learning techniques resilient to changes in handwriting styles. The output accuracy is dependent on completeness and quality of training dataset.
In 2011, seven deep CNNs were trained on the same data, pre-processed in different ways. Powerful GPUs made the task possible of carrying out deep learning effectively. The errors differed but were averaged. The result is comparable to human performance achieving a 0.27% error rate. The two problems with classical methods are thus eradicated. Now, neural networks can recognize any alphabet in any style and handwriting.
CapsNets- The future
CapsNets or Capsule Networks is a further advancement in the field thanks to the emerging research. The introduction of CapsNets provides another approach to machine learning solutions and the future of improved handwriting recognition. CapsNets are new to the landscape but can pick up the pace and absolve limitations of CNNs, improving the results by reducing the effects of spatial variance and the amount of data required for training. The former issue is resolved by CapsNets kernels used to determine features that work together, combining individual opinions of multiple capsules. It is necessary to have large datasets for recognizing handwriting for the most effective use of CNNs as the model requires a significant variance for high accuracy. CapsNets can work on small datasets without hampering efficiency.
In this article, we touched upon some of the machine learning applications that can turn handwriting into an entirely different output. Beyond text to text, text to speech, and text to images is also possible. Global Tech Council covers these aspects in the machine learning certification course. If you want to be a Certified Machine Learning Expert, then this is the way to go about!