The state of the art in online handwriting recognition

Tappert, Charles C.; Suen, Ching Y.; Wakahara, Toru

doi:10.1109/34.57669

Cited by 725 publications

(266 citation statements)

References 100 publications

Supporting

Mentioning

251

Contrasting

Unclassified

Order By: Relevance

“…In this sense, Post-processing is processing of the output from the preview recognition module [13]. This module could contain different postprocessors:  Set up a measure of confidence when the recognition output has several alternatives in order to improve automatic recognition or end-user selection.…”

Section: Post-processing Modulementioning

confidence: 99%

“…Depending on the application and the general purpose, the developer needs to pre-process before the data before any other step like recognition, animation or just final presentation to the user. All the techniques to refine the data are included on this module, for example: sampling, noise reduction (smoothing, filtering, interpolation, wild point correction, dehooking, dot reduction), normalization, affine transformations and stroke connection [12], [13].…”

Section: Modulesmentioning

confidence: 99%

See 1 more Smart Citation

Modular Architecture for Pen-Based Digital Ink on Blended Learning Applications

Neil¹,

Rosado²,

Esponda-Argüero³

et al. 2014

IJIET

View full text Add to dashboard Cite

Abstract-Pen-based technologies can be combined with different kinds of hardware and software to gain pedagogical benefits on blended learning applications. Particularly, digital ink and handwriting recognition furnish the convenience to access, navigate and manipulate data more easily. It is also well know that user interfaces for pen computing can be implemented in several ways, with different techniques and for several kinds of applications. A modular programming architecture for pen-based digital ink was developed in order to provide a robust, reliable, usable and sustainable multimedia technology for a blended learning application. We present this approach and the respective advantages.

show abstract

Section: Post-processing Modulementioning

confidence: 99%

Section: Modulesmentioning

confidence: 99%

Modular Architecture for Pen-Based Digital Ink on Blended Learning Applications

Neil¹,

Rosado²,

Esponda-Argüero³

et al. 2014

IJIET

View full text Add to dashboard Cite

show abstract

“…Although a lot of research work is available, most of it is on English alphabets and numerals which can be enclosed in standard rectangular structures [11][12][13]. Sufficient work has been done for the recognition of Chinese characters, Korean characters, Japanese characters [14][15].…”

Section: Kannada Ocr Systemmentioning

confidence: 99%

Neural Network based Bilingual OCR System: Experiment with English and Kannada Bilingual Documents

Patil¹

2011

IJCA

View full text Add to dashboard Cite

The paper presents the Neural Network based Bilingual OCR system which can read printed document images, written in two scripts of English and Kannada languages. Such systems are highly preferred in automation of multi-script, multi lingual document processing. The developed system includes document image pre-processor, dynamic feature extractor, neural network based script classifier, Kannada character recognition system and English character recognition system. Document image pre-processor, accepts the bilingual document image and performs grey to two tone conversion, segmentation into lines and words. Dynamic feature extractor extracts distinctive equal number of features from each separated word irrespective of size of the word. These features are accepted by probabilistic neural classifier and are sorted by script, Kannada and Roman. Developed Kannada character recognition system accepts these words and further segments each word into characters and maps the recognized characters into corresponding ASCII values of the chosen Kannada font. Similarly specifically developed English character recognition system, segments English words into characters and maps to corresponding ASCII value of the specific English font. Thus recognized English and Kannada characters are written into separate ASCII files language wise. The results are exciting and proved the effectiveness of the approach.

show abstract

“…See Tappert et al (1990) or Beigi (1993) for surveys of the common techniques. We have developed a SHARK recognition system based on the classic elastic matching algorithm (Tappert, 1982) which computes the minimum distance between two sets of points by dynamic programming.…”

Section: Shark Gesture Recognitionmentioning

confidence: 99%

In search of effective text input interfaces for off the desktop computing

Zhai¹,

Kristensson²,

Smith³

2005

Interacting with Computers

View full text Add to dashboard Cite

It is generally recognized that today's frontier of HCI research lies beyond the traditional desktop computers whose GUI interfaces were built on the foundation of display-pointing device-full keyboard. Many interface challenges arise without such a physical UI foundation. Text writingranging from entering URLs and search queries, filling forms, typing commands, to taking notes and writing emails and chat messages-is one of the hard problems awaiting for solutions in off-desktop computing. This paper summarizes and synthesizes a research program on this topic at the IBM Almaden Research Center. It analyzes various dimensions that constitute a good text input interface; briefly reviews related literature; discusses the evaluation methodology issues of text input; presents the major ideas and results of two systems, ATOMIK and SHARK; and points out current and future directions in the area from our current vantage point. q

show abstract

The state of the art in online handwriting recognition

Cited by 725 publications

References 100 publications

Modular Architecture for Pen-Based Digital Ink on Blended Learning Applications

Modular Architecture for Pen-Based Digital Ink on Blended Learning Applications

Neural Network based Bilingual OCR System: Experiment with English and Kannada Bilingual Documents

In search of effective text input interfaces for off the desktop computing

Contact Info

Product

Resources

About