Editing Handwriting recognition (section)

== Offline recognition ==
Offline handwriting recognition involves the automatic conversion of text in an image into letter codes that are usable within computer and text-processing applications. The data obtained by this form is regarded as a static representation of handwriting. Offline handwriting recognition is comparatively difficult, as different people have different handwriting styles. And, as of today, OCR engines are primarily focused on machine printed text and [[Intelligent character recognition|ICR]] for hand "printed" (written in capital letters) text.

=== Traditional techniques ===

==== Character extraction ====
Offline character recognition often involves scanning a form or document. This means the individual characters contained in the scanned image will need to be extracted. Tools exist that are capable of performing this step.<ref>[https://sourceforge.net/projects/javaocr/ Java OCR, 5 June 2010]. Retrieved 5 June 2010</ref> However, there are several common imperfections in this step. The most common is when characters that are connected are returned as a single sub-image containing both characters. This causes a major problem in the recognition stage. Yet many algorithms are available that reduce the risk of connected characters.

==== Character recognition ====
After individual characters have been extracted, a recognition engine is used to identify the corresponding computer character. Several different recognition techniques are currently available.

===== Feature extraction =====
[[Feature extraction]] works in a similar fashion to neural network recognizers. However, programmers must manually determine the properties they feel are important. This approach gives the recognizer more control over the properties used in identification. Yet any system using this approach requires substantially more development time than a neural network because the properties are not learned automatically.

=== Modern techniques ===

Where traditional techniques focus on [[image segmentation|segmenting]] individual characters for recognition, modern techniques focus on recognizing all the characters in a segmented line of text. Particularly they focus on [[machine learning]] techniques that are able to learn visual features, avoiding the limiting feature engineering previously used. State-of-the-art methods use [[Convolutional neural network|convolutional networks]] to extract visual features over several overlapping windows of a text line image which a [[recurrent neural network]] uses to produce character probabilities.<ref>Puigcerver, Joan. "Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?." Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. Vol. 1. IEEE, 2017.</ref>