This survey describes the state of the art of online handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Online versus offline recognition, digitizer technology, and handwriting properties and recognition problems are discussed. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. >

http://www.csis.pace.edu/~ctappert/dps/pdf/pen-tappert.pdf

The state of the art in online handwriting recognition

Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.

An overview of character recognition focused on off-line handwriting

Character recognition—a review

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines), automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade and dedicated to documents of historical interest.

https://www.cs.bgu.ac.il/~klara/ATCS111/TextLineSegmentation.pdf

Text line segmentation of historical documents: a survey

Zhai and Kristensson (2003) presented a method of speed-writing for pen-based computing which utilizes gesturing on a stylus keyboard for familiar words and tapping for others. In SHARK2:, we eliminated the necessity to alternate between the two modes of writing, allowing any word in a large vocabulary (e.g. 10,000-20,000 words) to be entered as a shorthand gesture. This new paradigm supports a gradual and seamless transition from visually guided tracing to recall-based gesturing. Based on the use characteristics and human performance observations, we designed and implemented the architecture, algorithms and interfaces of a high-capacity multi-channel pen-gesture recognition system. The system's key components and performance are also reported.

/pdf/shark2-a-large-vocabulary-shorthand-writing-system-for-pen-2uxe51xqmm.pdf

A. C. Downton

Papers

Preprocessing and presorting of envelope images for automatic sorting using OCR

Automatic recognition and transcription of Pitman's handwritten shorthand-an approach to shortforms