The funny thing about Evernote and other so-called note-taking apps: Most don’t support note-taking of the actual handwritten variety. That’s a shame, because discreetly jotting down shorthand during a critical client meeting trumps pounding away at an awkward virtual keyboard every time.

1. Handwriting To Text Pen
2. Handwriting To Text Android
3. Scan Handwriting To Text
4. Best Ocr For Handwriting
5. Handwriting To Text Mac

Fortunately for inveterate stylus lovers, a bevy of handwriting apps offer bells, whistles, and the ability to scribble all over your smartphone or tablet until your hand cramps. Here are a few worth checking out.

Notes Plus (iPad, $7.99): Few handwriting apps can top Notes Plus. Its powerful handwriting-recognition engine parses scrawl by fat fingers and slim styli alike, sharpens hastily drawn shapes, and enables you to edit notes or drag-and-drop whole sections to other areas. Notes Plus doesn’t skimp on the extras, either: It offers audio note support, sharing, PDF import/export capabilities, and automatic Dropbox synchronization. The only major downside is that it’s currently an iPad exclusive.

Penultimate (iPad, 99 cents): If you don’t need the extra features of Notes Plus, Penultimate is a highly regarded — and cheap — handwriting app that also happens to be an iPad exclusive. It lacks audio support, PDF import options, and multitouch capabilities, but the handwriting recognition is crisp and responsive. Like Notes Plus, Penultimate packs numerous note-sharing tools and plays nice with Dropbox. It also adds Evernote support, which compensates for its inability to turn notes into editable text, a major flaw of most handwriting apps. Exporting a PDF of a note to Evernote lets Evernote’s excellent optical character recognition technology shoulder the burden.

Antipaper Notes (Android tablets, free; $5.49 upgrade available): Not every tablet sports Apple’s iconic logo. Hordes of happy Android users say that Antipaper Notes is the best tablet-optimized handwriting app available for Google devices — and the basic version is free. The attractive-looking app mimics a real notepad and sports a wide variety of page and pen types. Writing appears quickly and flawlessly, but Antipaper Notes has some notable drawbacks: The number of pages is limited in the free version, and notes may only be exported as PNG or JPG image files (not PDFs) via email. (pictured)

PenSupremacy (Android, $1.49): PenSupremacy offers a little more flexibility than Antipaper Notes. The app works on Android phones and tablets, for one thing, and it can export PDFs of your notes via email, Evernote, Facebook, and various other means. The ability to import pictures into pages is another plus, as is voice dictation for audio notes. However, not everything is rosy in PenSupremacyland: Users say the app’s handwriting recognition can be sluggish and inaccurate, and there is no Undo option.

WritePad (iOS, $3.99; Android, $9.99): WritePad doesn’t even try to save your notes in shorthand. Basically, you scribble your notes on the screen, and when you pause WritePad converts them to text. Even better, the software adapts to your chicken scratch and grows more accurate the more you use it. The handwriting-recognition engine understands English, French, German, and Spanish, and text can be automatically translated into a dozen different languages. There’s even a built-in calculator. The more expensive Androidversion adds a WritePad virtual keyboard to your device that lets you hand-write emails, website URLs, text messages, and more, which the keyboard then converts to text. It’s all very intriguing, but beware: User reviews say the handwriting-to-text conversions are inaccurate until the app catches up with your penmanship. Customer service and Android device support can also be hit-and-miss.

To get started with handwriting recognition, click the keyboard icon and then click the keyboard button to the lower right of the onscreen keyboard that appears. Click the third icon in the popup.

Top 5 Software for OCR Handwriting. OCR – Optical Character Recognition is a recent mechanical translation method which converts images from handwritten text into editable text on your computer. Such as OCR scanned PDF or image-based PDF to native PDF. The technology has been developed in 1933, and it progressed every year. I have already answered similar question. Copying it here. Try which converts hand written scripts to digital text. If your content is in. Even better, the software adapts to your chicken scratch and grows more accurate the more you use it. The handwriting-recognition engine understands English, French, German, and Spanish, and text can be automatically translated into a dozen different languages. There’s even a built-in calculator. Handwriting Recognition on Surface Tablets. The Handwriting recognition on Surface tablets uses something called the Handwriting Panel which is really just a specific mode for the on-screen touch keyboard. When set to handwriting mode, your Surface will allow you to print text using a stylus or finger, which it will then convert to text and insert into your application or document. OCR software handwriting recognition uses OCR technology known as 'intelligent character recognition'. This software means that the converting engine is able to recognize different shapes and lines and see that they are, in fact, letters. Of course, OCR software handwriting recognition isn't yet infallible. With printed or cursive writing, in particular, the software is currently unable to render these types of.

Handwriting To Text Pen

This is only the tip of the handwriting-app iceberg. Which apps do you prefer? Do you even care about taking notes in shorthand? Please share your thoughts in the Comments section below.

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. 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 plausible words.

• 1Off-line recognition
  • 1.1Traditional techniques
    • 1.1.2Character recognition
• 2On-line recognition
  • 2.1General process
• 5See also

Off-line recognition[edit]

Off-line handwriting recognition involves the automatic conversion of text in an image into letter codes which are usable within computer and text-processing applications. The data obtained by this form is regarded as a static representation of handwriting. Off-line 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[edit]

Character extraction[edit]

Off-line character recognition often involves scanning a form or document written sometime in the past. This means the individual characters contained in the scanned image will need to be extracted. Tools exist that are capable of performing this step.^[1] 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[edit]

After the extraction of individual characters occurs, a recognition engine is used to identify the corresponding computer character. Several different recognition techniques are currently available.

Feature extraction[edit]

Feature extraction works in a similar fashion to neural network recognizers. However, programmers must manually determine the properties they feel are important.

• Is reflected x axisThis 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[edit]

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 which 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 an RNN uses to produce character probabilities.^[2]

Handwriting To Text Android

On-line recognition[edit]

On-line 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 which are usable within computer and text-processing applications.

The elements of an on-line handwriting recognition interface typically include:

• a pen or stylus for the user to write with.
• a touch sensitive surface, which may be integrated with, or adjacent to, an output display.
• a software application which interprets the movements of the stylus across the writing surface, translating the resulting strokes into digital text. And an off-line recognition is the problem.

General process[edit]

Scan Handwriting To Text

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

• preprocessing,
• feature extraction and
• classification

The purpose of preprocessing is to discard irrelevant information in the input data, that can negatively affect the recognition.^[3] This concerns speed and accuracy. Preprocessing usually consists of binarization, normalization, sampling, smoothing and denoising.^[4] The second step is feature extraction. Out of the two- or more-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[edit]

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^[5] and the Inforite point-of-sale terminal.^[6]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,^[7] CIC^[8] 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 has been ported to Mac OS X 10.2 or later in form of Inkwell (Macintosh).

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 Graffiti (Palm OS).

A Tablet PC is a special notebook computer that is outfitted with a digitizer tablet and a stylus, and allows a user to handwrite text on the unit's screen. The operating system recognizes the handwriting and converts it into typewritten 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[edit]

Initial software modules could understand print handwriting where the characters were separated. Author of the first applied pattern recognition program in 1962 was Shelia Guberman, then in Moscow.^[9] 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[edit]

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. Active areas of research include:

Best Ocr For Handwriting

• Online Recognition
• Offline Recognition
• Signature Verification
• Bank-Check Processing

Results since 2009[edit]

Handwriting To Text Mac

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.^[11] In particular, the bi-directional and multi-dimensionalLong short-term memory (LSTM)^[12][13] 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^[14] on the famous MNIST handwritten digits problem^[15] of Yann LeCun and colleagues at NYU.

See also[edit]

Lists[edit]

References[edit]

1. ^Java OCR, 5 June 2010. Retrieved 5 June 2010
2. ^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.
3. ^Huang, B.; Zhang, Y. and Kechadi, M.; Preprocessing Techniques for Online Handwriting Recognition. Intelligent Text Categorization and Clustering, Springer Berlin Heidelberg, 2009, Vol. 164, 'Studies in Computational Intelligence' pp. 25–45.
4. ^Holzinger, A.; Stocker, C.; Peischl, B. and Simonic, K.-M.; On Using Entropy for Enhancing Handwriting Preprocessing, Entropy 2012, 14, pp. 2324-2350.
5. ^Pencept Penpad (TM) 200 Product Literature, Pencept, Inc., 15 August 1982
6. ^Inforite Hand Character Recognition Terminal, Cadre Systems Limited, England, 15 August 1982
7. ^Users Manual for Penpad 320, Pencept, Inc., 15 June 1984
8. ^Handwriter (R) GrafText (TM) System Model GT-5000, Communication Intelligence Corporation, 15 January 1985
9. ^Guberman is the inventor of the handwriting recognition technology used today by Microsoft in Windows CE. Source: In-Q-Tel communication, June 3, 2003
10. ^S. N. Srihari and E. J. Keubert, 'Integration of handwritten address interpretation technology into the United States Postal Service Remote Computer Reader System' Proc. Int. Conf. Document Analysis and Recognition (ICDAR) 1997, IEEE-CS Press, pp. 892–896
11. ^2012 Kurzweil AI Interview with Jürgen Schmidhuber on the eight competitions won by his Deep Learning team 2009-2012
12. ^Graves, Alex; and Schmidhuber, Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K. I.; and Culotta, Aron (eds.), Advances in Neural Information Processing Systems 22 (NIPS'22), December 7th–10th, 2009, Vancouver, BC, Neural Information Processing Systems (NIPS) Foundation, 2009, pp. 545–552
13. ^A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, J. Schmidhuber. A Novel Connectionist System for Improved Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, 2009.
14. ^D. C. Ciresan, U. Meier, J. Schmidhuber. Multi-column Deep Neural Networks for Image Classification. IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012.
15. ^LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE, 86, pp. 2278-2324.

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