Want to extract data from handwritten forms? Head over to Nanonets and start building Handwriting OCR models for free! In this article we will be learning about the task of handwritten text recognition, it's intricacies and how we can solve it using deep learning techniques. Recognizing handwritten text is termed Intelligent Character Recognition(ICR) due to the fact that the algorithms needed to solve ICR need much more intelligence than solving generic OCR. Recent advancements in Deep Learning such as the advent of transformer architectures have fast-tracked our progress in cracking handwritten text recognition. Nevertheless it's a crucial problem to solve for multiple industries like healthcare, insurance and banking. The high variance in handwriting styles across people and poor quality of the handwritten text compared to printed text pose significant hurdles in converting it to machine readable text. Although OCR has been considered a solved problem there is one key component of it, Handwriting Recognition (Handwriting OCR) or Handwritten Text Recognition(HTR) which is still considered a challenging problem statement. This growth is driven by rapid digitization of business processes using OCR to reduce their labor costs and to save precious man hours. Optical Character Recognition(OCR) market size is expected to be USD 13.38 billion by 2025 with a year on year growth of 13.7 %.
0 kommentar(er)
