Cursive handwriting makes separation and recognition of characters even more challenging.Oftentimes text is not written in a strictly straight line.Poor quality of the source document due to degradation over time.Tremendous variability and ambiguity of strokes from person to person.Main challenges in recognizing handwritten text
In this article, we will be learning about the task of handwritten text recognition, its intricacies and how we can solve it using different deep learning techniques. This due to the fact that the algorithms needed to solve ICR need much more “intelligence” than solving generic OCR. In the industry, we usually speak of Intelligent Character Recognition (ICR) when talking about recognizing handwritten text. Relatively recent advancements in Deep Learning such as the advent of transformer architectures have fast-tracked our progress in solving handwritten text recognition.
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Nevertheless, it’s a crucial problem to solve for multiple industries like healthcare, insurance and banking and therefore, Parashift’s R&D activities in full swing. The high variance in handwriting styles across people and poor quality of the handwritten text compared to machine-printed text pose significant obstacles in converting it to machine-readable text formats. Handwriting Recognition or Handwritten Text Recognition (HTR) being one of them.
But also because of the increasingly mobile workforce and the often associated need for document processing via mobile devices. Although OCR has been considered a solved problem by a wide range of people, there are yet many challenges to solve. 10%. The increasing demand for document extraction software solutions is mainly due to compliance initiatives, the digitalization of document management and intentions to optimize operational costs. The Optical Character Recognition (OCR) respectively Document Capture software market size is expected to be USD 12.6 billion by 2027 with a year on year growth of approx.
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