Real-time recognition of handprinted text

Groner, Gabriel F.

doi:10.1145/1464291.1464355

Cited by 45 publications

(9 citation statements)

References 10 publications

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“…One of the first ideas to recognize symbols was using direction sequences from stroke motion alongside with lookup tables (Groner, 1968;Powers, 1973). Later, Rubine (1991a) and Smithies et al (1999) introduced a variety of statistical and geometric features (aspect ratio, stroke length, etc.)…”

Section: Recognizing Gesturesmentioning

confidence: 99%

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

et al. 2014

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Oxford University PressLeiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. doi:10.1093/iwc/iwu019. +34 963878172 Email: {luileito,valabau,vromero,ahector,evidal}@prhlt.upv.es This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study CATTI, a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based textediting interfaces, without worrying to verify the user intent onscreen.We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (less than 1% error rate) with very high performance (less than 1 ms of recognition times). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.

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Section: Recognizing Gesturesmentioning

confidence: 99%

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

et al. 2014

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“…Most handprinted character recognition procedures currently described in the literature [6][7][8][9][10][11][12][13][14] tend to be "algorithmic" in nature. Typically a set of numerical "features" or some sort of formal description is derived from the character image by an analysis which performs a large amount of calculation, independently of the nature of the image.…”

Section: The "Verifier" Character Recognizermentioning

confidence: 99%

Handprint recognition on the GRAFIX I:

Griffith¹

1974

Proceedings of the 1974 Annual Conference on - ACM 74

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This paper describes a commercial hardware/software system which reads handprinted documents. The system recognizes a full upper case alphabet, all ten digits, and several special characters, intermixed, with minimal constraints imposed on the style of printing.The recognition algorithm, which incorporates a large number of heuristic methods, is described. On a test of over sixty thousand previously unseen characters produced by untrained clerks, fewer than six percent of the characters were "rejected" as unrecognizable, and only a few tenths of a percent were misrecognized.

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“…predates the use of a mouse and graphical display by at least two decades, starting with the Stylator [12] and RAND tablet [16] systems of the 1950s and early 1960s.…”

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confidence: 99%