Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
  • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
  • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
  • G06F3/033—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
  • G06F3/0354—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor with detection of 2D relative movements between the device, or an operating part thereof, and a plane or surface, e.g. 2D mice, trackballs, pens or pucks
  • G06F3/03545—Pens or stylus
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
  • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
  • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
  • G06F3/033—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
  • G06F3/0346—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
  • G06V30/10—Character recognition
  • G06V30/32—Digital ink
  • G06V30/333—Preprocessing; Feature extraction
• • G—PHYSICS
  • G07—CHECKING-DEVICES
  • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
  • G07C9/00—Individual registration on entry or exit
  • G07C9/30—Individual registration on entry or exit not involving the use of a pass
  • G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
  • G07C9/35—Individual registration on entry or exit not involving the use of a pass in combination with an identity check by means of a handwritten signature

Definitions

• the present invention relates to computer input devices generally and more particularly to handwriting responsive computer input devices.
• handwriting analysis for alphanumeric input to a computer seeks to minimize the effect of the very features which are important for identity verification and to concentrate on universal handwriting characteristics which can be associated with given symbols independently of the individual writer.
• Currently existing and proposed systems providing handwriting analysis for alphanumeric input to a computer are generally geared towards recognition of how a symbol looks rather than how it is created. Accordingly, such systems employ digitizers or graphic tablets.
• Signature verification systems attempt to identify biometric characteristics of the writer and employ indications such as pressure and acceleration during writing.
• U.S. Patent 4,345,239 employs pen acceleration for use in a signature verification system.
• U.S. Patent 5.054,088 employs both acceleration and pressure data characteristics of handwriting for identity verification.
• pen acceleration is employed for signature verification because it is a personal feature, characteristic of each individual. Accordingly, pen acceleration has not been employed for alphanumeric input.
• U.S. Patent 4,751,741 describes pen-type character recognition apparatus which employs pen pressure data exclusively.
• the present invention seeks to provide improved handwriting information input apparatus.
• apparatus for reading handwriting including apparatus for sensing features of handwriting of an individual which features are highly characteristic of the individual but which also contain information relating to symbols being written and apparatus, which is configured for the individual, for providing a non-individual dependent output indicating the symbols being written in response to the sensed features.
• the apparatus for reading handwriting is contained in a hand-held housing.
• the apparatus for reading handwriting includes apparatus for wireless communication with a computer to which it inputs symbol data.
• the apparatus for reading handwriting does not require a tablet.
• the apparatus for reading handwriting communicates with the keyboard input of a computer. Additionally in accordance with a preferred embodiment of the present invention there is provided apparatus for reading handwriting including personalized hand-held apparatus for sensing acceleration during handwriting and providing an output indication of handwriting content in a non-personalized form. Further in accordance with a preferred embodiment of the present invention there is provided apparatus for reading handwriting including wireless hand-held apparatus for sensing handwriting and providing an output indication of the contents thereof. Additionally in accordance with a preferred embodiment of the present invention there is provided apparatus for reading handwriting including personally trainable hand-held apparatus for sensing motion during handwriting and providing an output indication of handwriting content.
• apparatus for reading handwriting in real time comprising a hand held housing, a motion sensor disposed in the housing, recognizing apparatus disposed within the hosing and receiving signals from said motion sensor for sensing a plurality of handwriting characteristics and symbol recognizing apparatus disposed in said housing receiving the outputs of the plurality of parallel recognizers for providing an indication of a handwritten symbol.
• apparatus for reading handwriting including hand-held apparatus for sensing motion during handwriting and providing an output indication of handwriting content in a form corresponding to that of a conventional keyboard.
• apparatus for reading handwriting including hand-held apparatus for sensing motion during handwriting and providing an output indication of handwriting content in a RS-232 compatible form.
• audio-visual apparatus including apparatus for providing a human sensible output including information i at least one of audio and visual form and having as an input element hand-held apparatus for sensing motion during handwriting of the type described hereinabove. Examples of such audio-visual apparatus include a video recorder and player, a stereo audio player and a television.
• portable information storage and retrieval apparatus including a portable computer memory and output device and having as an input element hand-held apparatus for sensing motion during handwriting of the type described hereinabove .
• Examples of such portable information storage and retrieval apparatus include a digital watch with memory, a computerized diary, a computerized dictionary and electronic telephone book.
• lock apparatus including locking apparatus responsive to a predetermined electronic input and having as an input element hand-held apparatus for sensing motion during handwriting of the type described hereinabove.
• Examples of such locking apparatus include door locks and vehicle door locks and ignitions.
• magnetic card activated apparatus including apparatus for reading a magnetic card and having as a verification input element, hand-held apparatus for sensing motion during handwriting of the type described hereinabove .
• Examples of such magnetic card activated apparatus include automatic teller apparatus and point of sales credit card acceptance units.
• Fig. 1 is a pictorial illustration of a handwriting reading device constructed and operative in accordance with a preferred embodiment of the present invention in an operative environment;
• Fig. 2 is a simplified illustration of a preferred mechanical structure of the handwriting reading device of the present invention
• Fig. 3 s a simplified block diagram illustration of the handwriting reading device of Figs. 2 and 3 ;
• Fig. 4 is a partially schematic, partially block diagram illustration of part of the apparatus of Fig. 3;
• Fig. is a block diagram illustration of part of the apparatus of Fig. 4.
• Figs. 6A and 6B are simplified flow charts illustrating operation of the handwriting reading device of Figs. 3 ⁇ 5 during handwriting reading.
• Fig. 6B illustrates the teaching process and
• Fig. 6B illustrates the recognition process.
• Fig. 1 illustrates a handwriting input device 10 constructed and operative in accordance with a preferred embodiment of the invention in a typical operating environment wherein it communicates by wireless communication with a computer 11, such as a notebook PC having an associated receiver 12, such as a model RB 1023 RF receiver, commercially available from RF Monolithics, Inc. of Dallas, Texas.
• Receiver 12 may communicate with computer 11 via the RS 232 port thereof or alternatively via the keyboard input thereof.
• the handwriting input device 10 which is illustrated in greater detail in Fig. 2, may be used on any writing surface or alternatively in the absence of a writing surface and does not require any special pad or writing substrate.
• the handwriting input device comprises a housing 13 in the general size and shape of an ordinary pen which is preferably provided with suitable indentations 14 for predetermined finger engagement.
• an ink reservoir and output point assembly 16 Disposed in housing 13 is an ink reservoir and output point assembly 16, which may be constructed and operative in any conventional manner. Alternatively no ink output may be provided.
• the accelerometer 20 is located interiorly of indentations 14.
• a typical accelerometer which meets the size and power requirements of the invention comprises at least two and preferably three mutually orthogonally mounted Model 3031 accelerometers commercially available from EuroSensor of 20 - 24 Kirby Street, London, England. Referring additionally to Fig. 3. it is seen that the output of the accelerometer 20 is supplied via an operational amplifier 24, such as a model LT1179. commercially available from Linear Technology Corporation of Milpitas, California, to a microcontroller 26, such as an Hitachi H8/536 microcontroller including an A/D converter. Microcontroller 26 is operative to extract a plurality of predetermined features of the acceleration sensed by accelerometer 20.
• a typical catalog of features extracted by the microcontroller 26 appears in the Tappert et al reference described hereinabove and is hereby incorporated by reference and also appears more explicitly in Appendix B.
• a preferred listing of software that provides the functionality of the microcontroller 26 appears in Appendix C.
• the microcontroller 26 provides the functionality of a bank of parallel recognizers 26.
• the parallel recognizers are operative to recognize 128 different symbols. They may also be operative to recognizers various graphic symbols.
• the parallel recognizers are also personalized by suitable training which is preferably carried out using the pen 10 and the computer 11 and involves subsequent downloading to the pen 12, so as to associate the various alphanumeric symbols with given acceleration derived features extracted by the microcontroller 26.
• the microcontroller 26 also provides the functionality of post-processing circuitry and is operative to select the most probable symbol from among those recognized by the bank of parallel recognizer and to encode it in a conventional universal code, such as ASCII, which is not in any way dependent on the personal handwriting characteristics of a given individual and which can be readily accepted by conventional computers.
• a conventional universal code such as ASCII
• the coded symbol output from microcontroller 26 is in a form compatible with or identical to the output conventionally received at the keyboard input of a conventional computer, such as a PC.
• the coded output of microcontroller 26 is transmitted to computer 11 in a wireless manner by a wireless transmitter 32, such as a model MB1003, which is also commercially available from RF Monolithics, Inc.
• the computer 11 is preferably supplied with a corresponding receiver which outputs directly to the keyboard input of the computer or any other suitable input, such as an RS 232 port. Alternatively a non-wireless communication connection may be provided.
• a suitable battery 33 is provided to power the apparatus located within housing 13. It will be appreciated that the handwriting 1 ⁇
• the reading apparatus of the present invention is preferably a hand-held, personalized "pen" which can be carried by the individual for whom it has been personalized and used with any computer having suitable communication facilities.
• the computer need not be personalized in any way, inasmuch as all of the handwriting recognition hardware and software is resident in the "pen”.
• Fig. 4 and Fig. 5 illustrate portions of the apparatus of Figs. 1 - 3 i differing levels of detail.
• the accelerometer 20, as noted above, preferably comprises three separate accelerometer modules, each including a bridge containing piezoresistive elements outputs via operational amplifier 24 to microcontroller 26 including A/D converter channels 44.
• the microcontroller 26 provides DSP functionality, represented by block 45 which is preferably operative to extract 32 features which are combinations of components of X-acceleration, Y-acceleration and up and down movements of the pen.
• the output of functional block 4 is supplied to the bank of parallel recognizers 26 which includes a plurality of fuzzy comparators 46, each of which receives from reference feature storage facility 47 reference features for a plurality of alphanumeric symbols in addition to the serial input from DSP circuitry 24 which contains vectors representing the extracted features.
• the outputs of the fuzzy comparators 46 are supplied to corresponding error accumulators 48.
• the outputs of the error accumulators 48 are supplied to a fuzzy associative memory 50 which receives threshold inputs from a threshold definer 5 .
• Threshold definer 52 receives data inputs together with control and timing inputs. It is appreciated that in accordance with a preferred embodiment of the invention, the structure of Figs. 4 and 5 is embodied in software resident in microcontroller 26, as exemplified in Appendix C.
• Figs. 6A and 6B are simplified flow charts illustrating operation of the DSP block 45 (Fig 4). Fig. 6A illustrates the teaching process and Fig. B illustrates the recognition process.
• the DSP block operates to perform double integration of X and Y acceleration to obtain not only velocities, but also positions and travel distances. In addition, non- linear positive and negative rotations are counted, located and their length recorded.
• Appendix D sets forth an alogorithm for personalization of the handwriting reading device of Figs. 3-5-
• the personalized functions are carried out using the pen 10 and the computer 11.
• the user initially writes each alpha-numeric symbol.
• the symbol is "read” by the device which determines X, Y and Z accelerations, and employs the accelerations to extract the features listed above, in a manner set forth generally in Fig. 6.
• Feature recognition is achieved by recognizing the centroids of the pen-strokes of the individual writing and by classifying of each character into 12-16 pen-stroke types.
• the character is considered to be a sequence of certain pen-strokes and is recognized as a fuzzy string over the pen-strokes alphabet.
• the fuzzy rules are downloaded to the microcontroller 26 in the pen 10.
• the extracted features are employed to adapt fuzzy rules.
• the fuzzy rules are employed in the operation of a symbol recognizer which provides output indication of a recognized symbol.
• the actual and recognized symbols are compared to provide a difference indication which is used to further adapt the fuzzy rules until an acceptable match between the actual and recognized symbol is attained.
• audio-visual apparatus including apparatus for providing a human sensible output including information in at least one of audio and visual form and having as an input element hand-held apparatus for sensing motion during handwriting of the type described hereinabove. Examples of such audio-visual apparatus include a video recorder and player, a stereo audio player and a television. Other home appliances such as washing machines and cooking apparatus may be operated along similar principles.
• portable information storage and retrieval apparatus including a portable computer memory and output device and having as an input element hand-held apparatus for sensing motion during handwriting of the type described hereinabove.
• portable information storage and retrieval apparatus include a digital watch with memory, a computerized diary, a computerized dictionary and electronic telephone book.
• lock apparatus including locking apparatus responsive to a predetermined electronic input and having as an input element hand-held apparatus for sensing motion during handwriting of the type described hereinabove. Examples of such locking apparatus include door locks and vehicle door locks and ignitions.
• magnetic card activated apparatus including apparatus for reading a magnetic card and having as a verification input element, hand-held apparatus for sensing motion during handwriting of the type described hereinabove.
• magnetic card activated apparatus include automatic teller apparatus and point of sales credit card acceptance units.
• the time domain signals were segmented into dis ⁇ crete pen strokes units and represented as vectors in a feature space. Those vectors were clustered, using a variety of clustering techniques. We found that in spite of the fact that the hand movements during writing could take any form or shape, a particular writer employs only a very limited set of pen strokes. The results of the clustering by var ⁇ ious methods, yields a limited set of only twelve to fourteen types of pen strokes that accounts for ⁇ Obrain supposedly chunks information to minimize the required attentional resources. - 15 - keywords: handwriting, human motor control. chunking, automatic- ity, connectiomsm
• the biological plausibility of an hand writing model involves two parts: The plausibility of the assumed neurological control, and the biomechanical properties of the hand.
• the preservation of the writing style while using dif ⁇ ferent muscles and even organs, is one of the most interesting questions.
• the automaticity of writing suggests a chunking mechanism, but this chunking mechanism is probably not in the motoric system of the hand, but somewhere in the upper control levels of the brain. Therefore, whenever we refer to the "hand”, we do it metaphorically. I.e. the "hand " represents the efferent mechanism that accomplishes the motoric control.
• Alexander et al. [3] raised the question whether the specific concept of a "motor program”, is an appropriate foundation for the development of biological plausible models of how the brain controls movements.
• Georgopolos [1] recorded electrical activ ⁇ ity of single neurons, and found command neurons in the monkey ' s motor cortex (precentral gyrus) that encode the direction of forelimb movement. The firing of these neurons was not associated with the contraction of a par ⁇ ticular muscle or with the force of the coordinate movement. Georgopoulos computed a vector by summing the firing frequencies of many neurons, and found that it is more correlated with the direction of movement than is the activity of any individual cell. The vector becomes evident several millisec ⁇ onds before the arm moves. He interpreted this result as evidence for motor neuron planing.
• Damasio and Damasio [4] discussed the linguistic behavior of patients with lesion in the left posterior temporal and inferior parietal cor ⁇ tex. It was found that such patients have problem in producing word forms from the available phonemes. Analyzing the accumulated empirical finding on language structures, gathered with assistance of imaging techniques like MRI (Magnetic Resonance Imaging) and PET (Positron Emmision Tomog ⁇ raphy), shows that linguistic activity like naming, involves the motor cortex activation together with anterior and posterior language centers in the left hemisphere. Writing is a language activity which involves a production cen ⁇ ter that forms words and activates the "command cells" in the motor cortex to produce pen-strokes sequences (letters) and written words. In the same way that speech is composed of a small set of phonemes, we argue that handwritten letters are composed of a small set of pen-strokes.
• connectionist view of schemas is that stored knowledge- atoms are dynamically assembled at the time of inference, into context- sensitive schemata.
• Rumelhart and McClelland (19S6) [14] proposed a tech ⁇ nique that suggests how an attentional selective mechanism might work. They propose the use of a set of mapping units which produce "dynami ⁇ cally programmable connections" and achieve focusing on different features on different times.
• Smolensky ( 19S6) maintains that schemata are coherent assemblies of knowledge atoms, where coherence or consistency is formalized under the name of harmony.
• Rumelhart [13] developed a system which learns to recognize cursive script as it is generated by a writer. This system learns from examples of cursive script produced by a number of writers and recorded. He collected approximately 1000 words from each of 58 writers. The average length of a word is about 8 characters, That sums up to nearly 500,000 examples of handwritten cursive characters. His results were encouraging and had been used in this research. While Rumelhart [13] was mainly interested in handwriting recognition, this article uses the same data to investigate the writing mechanism.
• the data were collected in the following manner. Each word in the corpus was recorded. It was then played to the writer who was instructed to write the word on a tablet digitizer. The resulting x coordinate, y coordinate and an indication of whether the pen was or was not on the paper were sampled each 10 milliseconds. The resolution ( more than 200 dpi) and the sampling rate (100 samples/sec) are those that are shown to be appropriate in the on-line hand-writing recognition literature ([16] ). The data was saved as files, and has been used for the analysis reported in this article. In addition to the data from Rumelhart 's experiment, several thousands pen strokes of Japanese handwriting were collected. Most of the data has been collected from hand written Hiragana characters, but some data has been collected during writing Kanji (idiographic) Japanese characters. Hi ⁇ ragana characters has the curved shapes of english hand printed characters, but without the ligature of cursive handwriting.
• a pen stroke was defined as a segments of the cursive writing signal, between two consecutive zero crossing of the vertical velocity of the pen movement. Each character was segmented to several segments or "pen- strokes".
• a typical writing rate in English is two letters per second. Writing Japanese characters (Hiragana), takes about the same time, and a typical Hiragana character can be written in 0.3 - 0.5 seconds.
• the principle of segmentation and feature extraction is to segment the con ⁇ tinuous signals into discrete segments and to represent each segment by a feature vector in the feature space.
• a pen-stroke is defined, there are many ways to represent it in a feature space.
• the on-line character recognition research employs several orthogonal transformations such as a discrete Fourier transform of the curve segments corresponding to the pen-strokes. That is, a pen-stroke is repre ⁇ sented by its Fourier coefficients obtained from its ⁇ ( ⁇ ) and y(t) signals. Es ⁇ sentially, any orthogonal transformation (e.g. Walsh transform, Karhunen- Loeve) could do in approximating the pen-strokes curves. That is, Plane curves can be approximated by orthogonal functions (Sinusoidal, polynomial or even square waves). This description can be also easily converted to the frequency domain, as was done in several studies of hand-writing recognition [16].
• orthogonal transformations such as a discrete Fourier transform of the curve segments corresponding to the pen-strokes. That is, a pen-stroke is repre ⁇ sented by its Fourier coefficients obtained from its ⁇ (
• segmentation and feature extraction methods depend of course, on the goal. If the goal is pattern recognition, then the segmentation and feature extraction are geared toward discrimination between the various patterns. In our case, we looked for a segmentation and features that are biological plau ⁇ sible. Consequently, we investigated only features that might be explained by the neurobiologicai control structures, like the direction of the strokes, their curvature etc.
• the segmentation and feature extraction mechanism employed was, to de ⁇ velop a model of the underlying handwriting process and to describe the data in terms of the parameters of the model.
• the model employed was derived from that of Hollerbach [10] and involved the assumption that the genera ⁇ tion process could be described as pair of coupled oscillators.
• the coupled harmonic oscillators is just one of the many models that exist. Actually, its basic assumption about the symmetric shape of the velocity profile (an half sinus shape), is probably an oversimplification.
• the literature about velocity profiles of pen-strokes usually assumes an asymetrical bell-shaped velocity profile. That is, a rapid-aimed movement described by a log-normal velocity profile is considered as the fundamental unit (stroke). More com ⁇ plex movements are described in terms of superimposed log-normal curves.
• the asymmetric nature of the velocity bell-shaped profile results from the global stochastic behavior of a large number of processes involved in velocity control.
• the y-axis consists of a series of up/down strokes whose velocity profile is assumed to be sinusoidal.
• the i-axis is also pendular with a constant velocity, c, to the right.
• Different characters are made by modulating the relative amplitudes, ⁇ and b, the relative phase, phi. and the relative frequency * - r and ⁇ y , in the x and y directions. It is, furthermore assumed that the parameters change only when the velocity in the y direction reaches zero (end of pen-stroke).
• a stroke as the motion between zero crossings in the y velocity - v y .
• segmentation occurs when the pen-state changes (from pen-down to pen-up or vice versa).
• Hollerbach's model was designed for synthesiz ⁇ ing handwritten-like character, by a second order mechanical system. This model does not try to imitate the human motor control, or to be used for analysis of human handwriting. However, as it is a control system model, some of the parameters might be interpreted in terms of the human biome- chanical system. For example, the parameter ⁇ . which designates the phase shift, can be interpreted as relating to the delay in the nervous-muscular control system. As such, it can have an important diagnostic value in motor diseases.
• Kanji characters on the other hand, have more short straight seg ⁇ ments, as can be seen in the following figures:
• the "mori” Kanji character in the picture is segmented to 27 pen-strokes (the last two pen-down strokes in the third "tree" are missing). Sixteen out of the twenty seven, are strokes in which the pen touched the paper, and 11 were just for moving the pen from one line to the other. Twelve sequences of "pen-down" strokes, correspond to the visible line segments in the character.
• the reconstructed Kanji character is depicted in the figure.
• the basic units of clustering were the pen-strokes, each of which was rep ⁇ resented as a point in an n dimensional space. Out of the six features that we extracted for each stroke only three have been used. First, we used only one frequency for the modeling, so the rare strokes that involved higher har ⁇ monies were removed. Second, we did not differentiated between Up-strokes and Down-strokes. Up strokes contain more high order harmonies, but we limited our analysis to the basic movements, and tried to ignore the fluctu ⁇ ation induced by the bio-mechanical control mechanism. The third feature that wasn't used was the mid-point. For the reconstruction of the pen-strokes in the spatial domain, the x-coordinate of the midpoint in each stroke was computed. However, our preliminary analysis showed that this variable was very highly correlated with the x variable. This preliminary analysis, yielded three variables that were almost uncorrelated: ⁇ r , ⁇ v and velocity. The dimension of the space were:
• the clustering employed a two phase strategy. First, a fast "nearest centroid sorting" algorithm was employed to reveal the clusters in the large data set. Then, the resulting centroids of the clusters have been submitted to different hierarchical clustering methods. The first phase algorithm was
• Figure 8 Clustering of 25,000 strokes of the same writer. Gray clusters represent down strokes.
• Figure 9 Thirteen centroid pen-strokes of an individual writer, including their relative frequencies. - 30 - sensitive to outlier strokes, that formed separate clusters. This was the reason why we got many very small clusters. These clusters accounted for less than lOof the observations. They were considered to be noise, or very exeptional pen strokes, and have been removed so not to influence the representativeness of the centroids of the large clusters.
• the second phase included clustering of the resulting centroids using ten different methods. We distinguished between methods that yield compact hyperspherical clusters, and those that can detect elongate clusters. We start with the first group of eight clustering methods:
• the different methods tend to favor different characteristics such as size, shape or dispersion.
• methods based on the least-squares cri ⁇ terion such as k-means or Ward's minimum variance method, tend to find clusters with roughly the same number of observations in each cluster.
• Aver ⁇ age linkage is biased toward finding clusters of equal variance.
• the clustering methods which are based on nonparametric density estimation, like the single linkage, will be discussed later in this chapter.
• Figure 10 Hierarchical (compact) clustering of the 12 pen-strokes centroids of a particular writer
• the above method revealed the following tree-based partition of the set of the basic twelve pen-strokes (of a particular writer).
• the horizontal-left strokes are one such a group, long down strokes are another group.
• the horizontal strokes themselves are subdivided to horizontal-left directed strokes, and horizontal right and up directed strokes.
• the high velocity C shaped strokes are part of circles or ovals. It should be noticed that for a specific writer, a certain stroke is always accomplished in the same way. For example, an horizontal short stroke, like crossing a t, will be done always as left directed strokes. Someone else could use only horizontal right directed strokes for that pur ⁇ pose.
• Figure 11 Hierarchical (Density linkage) clustering of the 12 pen-strokes of the same writer
• the clustering methods that employ nonparametric density estimation can detect also elongated cluster shapes. These clustering techniques yielded two distinct super clusters: the “down and long pen-strokes” , and the “up and right strokes”.
• the down strokes are those that form the "back-bone” of the English characters, while the up-right strokes are typically those that are used as ligature.
• any writer has a specific set of pen strokes that char ⁇ acterize the writer. While the same writer will have similar pen-strokes, in writing different languages, the frequency of appearance of a specific pen strokes depends, of course, on the language.
• POD Pen-strokes Ordering Diagram
• Figure 12 The centroids of the pen strokes of a writer, for English cursive writing.
• the pen-strokes are ordered according to their ⁇ y values, from up ⁇ strokes to down strokes
• Figure 13 The centroids of the pen strokes of a Japanese writer, for Japanese Hiragana characters.
• the pen-strokes are ordered according to their v y val ⁇ ues, from up-strokes to down strokes
• Figure 14 The centroids of the pen strokes of a Japanese writer, for english characters.
• the pen-strokes are ordered according to their v v values, from up-strokes to down strokes
• feat['l3] number of spaces where out_rot » 0.1 fe ⁇ t['»'»_ • number of spaces where out_rot-2.3 featC ⁇ .
• number of spaces where out_rot « ' «,5 featC.6 ⁇ number of spaces where out_rot»6,7 number of min X. ⁇ number of max X.
• number of min Y. • number of max Y. feat[5-] sui length of spaces. feat[52] • relative position of spaces.
• FILE *f_point ; struct FEATURES feat; struct FEATURES min_feat; struct FEATURES oax ⁇ feat; int featO; int first feat-1; int file_ ⁇ dxl « 2,file_ndx2-0; char Ctemp[10j; char Binfile[30].infile[3O].minfile[3O].oaxfile[3O]; char SymId[ll],BSyaId[ll];
• prev_y-y prev pen a p; ⁇ ⁇ else in**;
• arr_cor[i] .x_cor arr_cor[i-l].x_cor; arr_cor[i] .y_cor a arr_cor[i-l].y_cor; arr_cor[i].pen_status»0;
• This module includes general purpose procedures which are :
• def_quart a funtion that recieves two numbers , and returns the quarter of these two numbers in the range of ⁇ -7.
• case 1 ( ( # feat) .feat[29])**; break; case 7 ⁇ (( • feat).feat[30])**; break; default. break ;
• This aodule is for calculating and aanipulatlng extreauas . it includes four procedures :
• find_aax_extra To find the aax/ain X-coordinate and the aax/ain Y-coordinate of the syabol.
• analyse_ ⁇ xtreauas A procedure to analyse the extreauas found so far, mainly this procedure aarks close extreauas as suspecious extreauas.
• the input of this procedure is the array of coordinates which holds the coordinates of the syabol , and the nuaber of these points , it fills the special of points (i.e. , extreaum ⁇ or change in pen) in the array arr_extreauas , it returns the nuaber of special points it found.
• PPSy M (arr_cor[Pndx-l].y_cor-arr_cor[Pndx-2].y ⁇ cor) ; a-PPSx*PSy-PPSy*PSx; b-PPSx*PSx*PPSy PSy; arr extr ⁇ aoas[indE] .in rot-def quart(b.a); ⁇ indE-->; PSx-Sx; PSy «Sy;
• PSx- arr_cor[LastNdx-2] .x_cor-arr_cor[LastNdx-3] .x_cor) ;
• PSy- arr_cor[ La ⁇ tNdx-2] . y_cor-arr_cor[ La ⁇ tNdx-3] .y ⁇ cor) ; arr_ ⁇ xtr ⁇ aoBs [indE] . dir-d ⁇ f_quartTSx,Sy) ; a-PSx*Sy-PSy*Sx; ⁇ b-PSx Sx*PSy*Sy; arr_extreaoas[indE].in_rot-def_quart(b,a) ; arr_extremoms[indE].out_rot-15T return (indE) ;
• FILE *fp char space[8] ,Ctemp[8]; int teap; strcpy (space,”space”) ; t ⁇ ap-ain_feat.f ⁇ at[3]; itoa (t ⁇ ap.Ct ⁇ p.lO) ; strcat (space,Cteap) ;
• the program reads the file temp.ltr from the disk , and calculates the matching probability for every symbol to the symbol defined in the file temp.ltr , and then stores the vector of probabilties for all symbols in the file data.in .
• ⁇ # include " main . h "
• This aodule includes general purpose procedures which are :
• def_quart a funtion that recieves two numbers , and returns the quarter of these two numbers in the range of 0-7.
• (2) div_round a function to calculate the round nuaber of the division of two Integer nuabers
• int div round (int a. int t>) ⁇ int i-0; int tempi, c ⁇ mp2; teapl-a; teap2-b; a-abs(a) ; if (b-- ⁇ ) return (MAXINT) ; whil ⁇ (a>b)
• case 1 ( ( # feat) ,feat[29])**; ⁇ ak; case 7 : ( (*feat) .f ⁇ at[30])**; break; default: break ;
• This aodule is for calculating and aanipulatlng extreauas , it Includes four procedures :
• the input of this procedure is the array of coordinates which holds the coordinates of the syabol , and the nuaber of these points , it fills the special of points (i.e. , extreaums or change in pen) in the array arr_extremums , it returns the number of special points it found.
• int calc_ ⁇ xtr ⁇ moa ⁇ (struct TABLE_EXTREMOMS arr_extr ⁇ aoas[], struct POINT arr_cor[],int ⁇ nua_of_points) unsigned int indE; unsigned int ndx; unsigned p ⁇ n_wa ⁇ _up; int crnt_quart,prev_quart; int crnt_rot,prev_rot; int Sx.Sy.PSx.PSy.a.b; int first_point-l; int Pndx- ⁇ ; unsigned int La ⁇ tNdx; int 1; indE- ⁇ ;
• pen_sts «extrm[i] .pen_sts; xc_extrm[ j .dir" ⁇ xtro[i] .dir; xc_extn[ j .in_rot -extrm[i] .in_rot; xc_extrm[ j out_rot-extrm[i] . out_rot ; irstP--FALSE) ect_extrm (arr_cor,extrm, exc extra,i,Pi,j) ; tP-TRUE; ].pen_sts «0) FirstP-TRUE;
• nuo_points prepare (x,tmin_x,tmax_x,tmin_y,tmax_y) ; select_beg(center,min_x,nax_ ,ain_y,max_y) ;
• This procedure gets the points from file or from other input device and calculates the maxi ⁇ ms and miniauas fo each dim ⁇ ntion (two dimentions in this example •/ int prepare nt xc[N_P0INT][DIMENSION] ,int min_x,int •max_x, int # ain y.int *max_y) ⁇ ⁇

Landscapes

• Engineering & Computer Science (AREA)
• Theoretical Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• General Engineering & Computer Science (AREA)
• Human Computer Interaction (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Multimedia (AREA)
• Character Discrimination (AREA)
• Inspection Of Paper Currency And Valuable Securities (AREA)
• Electrochromic Elements, Electrophoresis, Or Variable Reflection Or Absorption Elements (AREA)
• Eye Examination Apparatus (AREA)

Applications Claiming Priority (1)

Publications (2)

Family

ID=22231442

Family Applications (1)

Country Status (5)

Families Citing this family (16)

Family Cites Families (3)

• 1992
  • 1992-10-13 WO PCT/US1992/008703 patent/WO1994009447A1/fr not_active Application Discontinuation
  • 1992-10-13 EP EP92922519A patent/EP0664912A4/fr not_active Withdrawn
  • 1992-10-13 CA CA002146850A patent/CA2146850A1/fr not_active Abandoned
  • 1992-10-13 AU AU27959/92A patent/AU2795992A/en not_active Abandoned
  • 1992-10-13 JP JP6509931A patent/JPH08507886A/ja active Pending

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events