Handwriting recognition

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Signature of country star Tex Williams Signiture of country stat, Tex Williams- 2013-04-07 11-37.jpg
Signature of country star Tex Williams

Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. [1] [2] The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available. A handwriting recognition system handles formatting, performs correct segmentation into characters, and finds the most possible words.

Contents

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 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. [3] 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 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 networks to extract visual features over several overlapping windows of a text line image which a recurrent neural network uses to produce character probabilities. [4]

Online recognition

Online handwriting recognition involves the automatic conversion of text as it is written on a special digitizer or PDA, where a sensor picks up the pen-tip movements as well as pen-up/pen-down switching. This kind of data is known as digital ink and can be regarded as a digital representation of handwriting. The obtained signal is converted into letter codes that are usable within computer and text-processing applications.

The elements of an online handwriting recognition interface typically include:

The process of online handwriting recognition can be broken down into a few general steps:

The purpose of preprocessing is to discard irrelevant information in the input data, that can negatively affect the recognition. [5] This concerns speed and accuracy. Preprocessing usually consists of binarization, normalization, sampling, smoothing and denoising. [6] The second step is feature extraction. Out of the two- or higher-dimensional vector field received from the preprocessing algorithms, higher-dimensional data is extracted. The purpose of this step is to highlight important information for the recognition model. This data may include information like pen pressure, velocity or the changes of writing direction. The last big step is classification. In this step, various models are used to map the extracted features to different classes and thus identifying the characters or words the features represent.

Hardware

Commercial products incorporating handwriting recognition as a replacement for keyboard input were introduced in the early 1980s. Examples include handwriting terminals such as the Pencept Penpad [7] and the Inforite point-of-sale terminal. [8] With the advent of the large consumer market for personal computers, several commercial products were introduced to replace the keyboard and mouse on a personal computer with a single pointing/handwriting system, such as those from Pencept, [9] CIC [10] and others. The first commercially available tablet-type portable computer was the GRiDPad from GRiD Systems, released in September 1989. Its operating system was based on MS-DOS.

In the early 1990s, hardware makers including NCR, IBM and EO released tablet computers running the PenPoint operating system developed by GO Corp. PenPoint used handwriting recognition and gestures throughout and provided the facilities to third-party software. IBM's tablet computer was the first to use the ThinkPad name and used IBM's handwriting recognition. This recognition system was later ported to Microsoft Windows for Pen Computing, and IBM's Pen for OS/2. None of these were commercially successful.

Advancements in electronics allowed the computing power necessary for handwriting recognition to fit into a smaller form factor than tablet computers, and handwriting recognition is often used as an input method for hand-held PDAs. The first PDA to provide written input was the Apple Newton, which exposed the public to the advantage of a streamlined user interface. However, the device was not a commercial success, owing to the unreliability of the software, which tried to learn a user's writing patterns. By the time of the release of the Newton OS 2.0, wherein the handwriting recognition was greatly improved, including unique features still not found in current recognition systems such as modeless error correction, the largely negative first impression had been made. After discontinuation of Apple Newton, the feature was incorporated in Mac OS X 10.2 and later as Inkwell.

Palm later launched a successful series of PDAs based on the Graffiti recognition system. Graffiti improved usability by defining a set of "unistrokes", or one-stroke forms, for each character. This narrowed the possibility for erroneous input, although memorization of the stroke patterns did increase the learning curve for the user. The Graffiti handwriting recognition was found to infringe on a patent held by Xerox, and Palm replaced Graffiti with a licensed version of the CIC handwriting recognition which, while also supporting unistroke forms, pre-dated the Xerox patent. The court finding of infringement was reversed on appeal, and then reversed again on a later appeal. The parties involved subsequently negotiated a settlement concerning this and other patents.

A Tablet PC is a notebook computer with a digitizer tablet and a stylus, which allows a user to handwrite text on the unit's screen. The operating system recognizes the handwriting and converts it into text. Windows Vista and Windows 7 include personalization features that learn a user's writing patterns or vocabulary for English, Japanese, Chinese Traditional, Chinese Simplified and Korean. The features include a "personalization wizard" that prompts for samples of a user's handwriting and uses them to retrain the system for higher accuracy recognition. This system is distinct from the less advanced handwriting recognition system employed in its Windows Mobile OS for PDAs.

Although handwriting recognition is an input form that the public has become accustomed to, it has not achieved widespread use in either desktop computers or laptops. It is still generally accepted that keyboard input is both faster and more reliable. As of 2006, many PDAs offer handwriting input, sometimes even accepting natural cursive handwriting, but accuracy is still a problem, and some people still find even a simple on-screen keyboard more efficient.

Software

Early software could understand print handwriting where the characters were separated; however, cursive handwriting with connected characters presented Sayre's Paradox, a difficulty involving character segmentation. In 1962 Shelia Guberman, then in Moscow, wrote the first applied pattern recognition program. [11] Commercial examples came from companies such as Communications Intelligence Corporation and IBM.

In the early 1990s, two companies  ParaGraph International and Lexicus  came up with systems that could understand cursive handwriting recognition. ParaGraph was based in Russia and founded by computer scientist Stepan Pachikov while Lexicus was founded by Ronjon Nag and Chris Kortge who were students at Stanford University. The ParaGraph CalliGrapher system was deployed in the Apple Newton systems, and Lexicus Longhand system was made available commercially for the PenPoint and Windows operating system. Lexicus was acquired by Motorola in 1993 and went on to develop Chinese handwriting recognition and predictive text systems for Motorola. ParaGraph was acquired in 1997 by SGI and its handwriting recognition team formed a P&I division, later acquired from SGI by Vadem. Microsoft has acquired CalliGrapher handwriting recognition and other digital ink technologies developed by P&I from Vadem in 1999.

Wolfram Mathematica (8.0 or later) also provides a handwriting or text recognition function TextRecognize.

Research

Method used for exploiting contextual information in the first handwritten address interpretation system developed by Sargur Srihari and Jonathan Hull Address Recognition.png
Method used for exploiting contextual information in the first handwritten address interpretation system developed by Sargur Srihari and Jonathan Hull

Handwriting recognition has an active community of academics studying it. The biggest conferences for handwriting recognition are the International Conference on Frontiers in Handwriting Recognition (ICFHR), held in even-numbered years, and the International Conference on Document Analysis and Recognition (ICDAR), held in odd-numbered years. Both of these conferences are endorsed by the IEEE and IAPR. In 2021, the ICDAR proceedings will be published by LNCS, Springer.

Active areas of research include:

Results since 2009

Since 2009, the recurrent neural networks and deep feedforward neural networks developed in the research group of Jürgen Schmidhuber at the Swiss AI Lab IDSIA have won several international handwriting competitions. [13] In particular, the bi-directional and multi-dimensional Long short-term memory (LSTM) [14] [15] of Alex Graves et al. won three competitions in connected handwriting recognition at the 2009 International Conference on Document Analysis and Recognition (ICDAR), without any prior knowledge about the three different languages (French, Arabic, Persian) to be learned. Recent GPU-based deep learning methods for feedforward networks by Dan Ciresan and colleagues at IDSIA won the ICDAR 2011 offline Chinese handwriting recognition contest; their neural networks also were the first artificial pattern recognizers to achieve human-competitive performance [16] on the famous MNIST handwritten digits problem [17] of Yann LeCun and colleagues at NYU.

Benjamin Graham of the University of Warwick won a 2013 Chinese handwriting recognition contest, with only a 2.61% error rate, by using an approach to convolutional neural networks that evolved (by 2017) into "sparse convolutional neural networks". [18] [19]

See also

Lists

Related Research Articles

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Artificial neural networks are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.

<span class="mw-page-title-main">Optical character recognition</span> Computer recognition of visual text

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo or from subtitle text superimposed on an image.

<span class="mw-page-title-main">Graphics tablet</span> Computer input device

A graphics tablet is a computer input device that enables a user to hand-draw images, animations and graphics, with a special pen-like stylus, similar to the way a person draws images with a pencil and paper. These tablets may also be used to capture data or handwritten signatures. It can also be used to trace an image from a piece of paper that is taped or otherwise secured to the tablet surface. Capturing data in this way, by tracing or entering the corners of linear polylines or shapes, is called digitizing.

<span class="mw-page-title-main">Newton OS</span> Discontinued operating system by Apple Inc.

Newton OS is a discontinued operating system for the Apple Newton PDAs produced by Apple Computer, Inc. between 1993 and 1997. It was written entirely in C++ and trimmed to be low power consuming and use the available memory efficiently. Many applications were pre-installed in the ROM of the Newton to save on RAM and flash memory storage for user applications.

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled.

<span class="mw-page-title-main">Digital pen</span> Input device

A digital pen is an input device which captures the handwriting or brush strokes of a user and converts handwritten analog information created using "pen and paper" into digital data, enabling the data to be utilized in various applications. This type of pen is usually used in conjunction with a digital notebook, although the data can also be used for different applications or simply as a graphic.

The term PenPad was used as a product name for a number of Pen computing products by different companies in the 1980s and 1990s. The earliest was the Penpad series of products by Pencept, such as the PenPad M200 handwriting terminal, and the PenPad M320 handwriting/gesture recognition tablet for MS-DOS and other personal computers.

<span class="mw-page-title-main">Long short-term memory</span> Artificial recurrent neural network architecture used in deep learning

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<span class="mw-page-title-main">Pen computing</span> Uses a stylus and tablet/touchscreen

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<span class="mw-page-title-main">Pencept</span>

Pencept, Inc. was one of a small number of pioneering companies in the 1980s developing and marketing technology known as pen computing.

The history of tablet computers and the associated special operating software is an example of pen computing technology, and thus the development of tablets has deep historical roots. The first patent for a system that recognized handwritten characters by analyzing the handwriting motion was granted in 1914. The first publicly demonstrated system using a tablet and handwriting recognition instead of a keyboard for working with a modern digital computer dates to 1956.

<span class="mw-page-title-main">Microsoft Tablet PC</span> Microsofts former line of tablets

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<span class="mw-page-title-main">MNIST database</span> Database of handwritten digits

The MNIST database is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. It was created by "re-mixing" the samples from NIST's original datasets. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels.

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References

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  6. Holzinger, A.; Stocker, C.; Peischl, B. and Simonic, K.-M.; On Using Entropy for Enhancing Handwriting Preprocessing , Entropy 2012, 14, pp. 2324–2350.
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  9. Users Manual for Penpad 320, Pencept, Inc., 15 June 1984
  10. Handwriter (R) GrafText (TM) System Model GT-5000, Communication Intelligence Corporation, 15 January 1985
  11. Guberman is the inventor of the handwriting recognition technology used today by Microsoft in Windows CE. Source: In-Q-Tel communication, June 3, 2003
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  13. 2012 Kurzweil AI Interview Archived 31 August 2018 at the Wayback Machine with Jürgen Schmidhuber on the eight competitions won by his Deep Learning team 2009-2012
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  18. "Sparse Networks Come to the Aid of Big Physics". Quanta Magazine . June 2023. Retrieved 17 June 2023.
  19. Graham, Benjamin. "Spatially-sparse convolutional neural networks." arXiv preprint arXiv:1409.6070 (2014).