CN1604124A - Method and system for compressing hand-written character template - Google Patents
Method and system for compressing hand-written character template Download PDFInfo
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- CN1604124A CN1604124A CNA031255736A CN03125573A CN1604124A CN 1604124 A CN1604124 A CN 1604124A CN A031255736 A CNA031255736 A CN A031255736A CN 03125573 A CN03125573 A CN 03125573A CN 1604124 A CN1604124 A CN 1604124A
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- 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
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- 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
- G06V30/22—Character recognition characterised by the type of writing
- G06V30/226—Character recognition characterised by the type of writing of cursive writing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
- G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
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- 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
- G06V30/19—Recognition using electronic means
- G06V30/191—Design or setup of recognition systems or techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06V30/1914—Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries, e.g. user dictionaries
Abstract
It is a method and system to compress written character model plate, which comprises the following: one code generating module 105 to generate a code book 125; one model plate compressing module 120 connected with code generating module to compare the non-compressed model character eigenvector 110 and code book 125 to provide a compression model plate of model character. Or one plate matching module is connected with the model compressing module to compare the distance between the non-compressed input character eigenvector 130 and the model character plate to provide preparation characters.
Description
Invention field
The present invention relates generally to the electronic recognition of handwritten text.The present invention is specially adapted to, but is not limited to compress the hand Character mother plate to use in having the portable electric appts of finite memory.
Background of invention
There are bothersome technological difficulties in the computer Recognition of print text decades.Up to date, many optical character identification (OCR) technology just is modified so that can degree of accuracy handle the text that scans.Yet this OCR technology only can be explained the text of being printed by typewriter or electronic printer usually.The computer Recognition of hand-written character remains a very difficult technologies challenge.
The prior art that is used to discern hand-written character generally includes the high resolving power template, and this template can be caught the relevant time dimension of the action with the book text except that two physical dimensions of text writing.This template utilizes electronic tablet to make usually, this figure input template recording text stroke when pen or input pen contact this figure input template.Another technology comprises the use electronic pen, and when character was write, this electronic pen write down moving of nib.The feature of inputting characters by handwriting is mated with the model Character mother plate that uses the different mode recognition technology then.The model Character mother plate comprises the assembly average of the proper vector of the numerous input samples that come from each character.
It is sizable needing the storer of the digitized hand-written character of the above-mentioned multidimensional of storage.The model template dictionary that is used for comparing with input template becomes very big.But the consumer requires Handwritten Digits Recognition to use sustainable development.Particularly exist for small hand held devices, for example the requirement of the Handwritten Digits Recognition ability of PDA(Personal Digital Assistant).This equipment has very limited storer and processing power.
The prior art that is used to compress the size of above-mentioned hand-written data comprises hand-written character is converted into multidimensional characteristic vectors.Store this feature vectors and compare common needs storage space still less with the corresponding hand-written pixel data of storage.Yet this feature vectors is compared with existing storer in handheld device still needs more storage space.Particularly for big character set, for example: Chinese character set can comprise more than 10,000 characters, and what the size of correlate template can be very is big.So need the improved method that is used to compress the hand-written character template.
Summary of the invention
According to an aspect of the present invention, a kind of method of compressing the hand-written character template is provided, this method may further comprise the steps: produce the code book (codebook) that comprises vector, the center of the not compact model character feature vector group that is provided by the model Character mother plate is provided described vector; And the compression template that described not compact model character feature vector and described code book is compared the character that supplies a model.
This method can preferably include to be calculated described not compact model character feature vector group and not to compress distance between the input character proper vector, and provide the candidate characters of preceding n (top-n) by calculating the described distance of not compressing between input character proper vector and the described model Character mother plate, wherein n is based on the numerical value of a threshold value.
Provide the step of not compressing the input character proper vector can comprise the step of from standardization input character template, extracting feature.
Code book preferably includes and is no more than 256 groups.
Not compressing the input character proper vector can be 8 dimensional vectors.
Can provide the sorting technique of described candidate characters to calculate the described distance of not compressing between input character proper vector and the described model Character mother plate by use, n candidate characters before providing.
Candidate characters can be provided in character identifying method.
Foundation on the other hand, the present invention includes the system that is used to compress the hand-written character template, this system comprises: a code book generator module that is used to produce code book, wherein said code book comprise the vector at the center of the not compact model character feature vector group that definition is provided by the model Character mother plate; And a template compression module, be operably connected to described code book generator module, be used for described not compact model character feature vector is compared so that the compression template of the character that supplies a model with described code book.
System preferably includes a template matches module that is operably connected to described template compression module, and being used for provides candidate characters by the distance of not compressing between input character proper vector and the described model Character mother plate.
This system also can comprise apart from the look-up table generator.
At this instructions, comprise in claims, term " " comprising " or similarly the term meaning be meant non-exclusive comprising, for example: comprising that the method for an element tabulation or device not only comprise also can comprise other unlisted elements by the element that those are listed.
Description of drawings
Put into practice in order more easily to understand the present invention and to make it, below in conjunction with the description of drawings preferred embodiment, wherein identical Reference numeral is represented components identical, wherein:
Fig. 1 is the schematic block diagram according to the software element of system of the present invention;
Fig. 2 is the general flow figure of explanation according to the method for compression hand-written character template of the present invention;
Fig. 3 is the schematic block diagram of the three-dimensional template of hand-written character;
Fig. 4 is the process flow diagram of summarizing the method be used to provide proper vector;
Fig. 5 is the process flow diagram that is used to produce the method for code book;
Fig. 6 is the process flow diagram of method that is used to produce the compression template of model character;
Fig. 7 is the process flow diagram that is used to provide the method for candidate characters; And
Fig. 8 is the schematic block diagram of compact model Character mother plate.
DETAILED DESCRIPTION OF THE PREFERRED
Referring to Fig. 1, there is shown the schematic block diagram of the software element of the system 100 that is used to compress the hand-written character template according to the preferred embodiment of the invention.Referring to Fig. 2, Fig. 2 is to use the software element of this system 100 to compress the general flow figure of the method for hand-written character.Software element comprises: a code book generator module 105, it produces not compact model character feature vector 110 and this proper vector 110 is categorized as group 115 from the model Character mother plate.This model Character mother plate comprises the assembly average of the proper vector of the numerous input samples that come from each character.Compact model character feature vector 110 does not comprise the proper vector of all characters that are used for character set.For some language, for example Chinese has the character more than 10,000 in a character set.Indexed so that form a code book 125 (step 210) in vectorial center of the group 115.Next, a template compression module 120 is arranged.Come from the code of code book 125 and this not compact model character feature vector 110 compare the compression template (step 215) that produces model character 135.
Referring to Fig. 3, showed the synoptic diagram that is input to an input of the present invention among the figure, it comprises digitized, high-resolution, the three-dimensional information visual 300 of a hand-written character 305.The bidimensional physical features and the time dimension that provides about the directional information of hand-written stroke of character 305 is provided this presentation of information 300.The level of character 305 and the stroke direction of vertical features are illustrated by arrow 310.Be used to produce three-dimensional image, for example the method for presentation of information 300 is being known in the art, thus shown in the step of method only be summarized in this.
Fig. 3 has illustrated also the presentation of information 300 of digitizing character 305 is how to be converted into not compress input character proper vector 130.The pixel of character 305 at first be suitable for grid 315 and by standardization so that the size of character 305 and the model character that is used to produce compact model character feature vector 110 not measure-alike.Each unit of grid 315 is subdivided into uniform refined net then, and is analyzed then so that extract proper vector 130.In the embodiment shown in fig. 3, each grid 320 comprises 7 * 7 unit.An example of proper vector 130 is octuple direction vectors.Vector each dimension of 130 is corresponding to the specified stroke direction of eight arrows 325 that are shown in Fig. 3, and these arrows are by being that increment is divided a circle and produced with 45 degree.One skilled in the art will know that according to the present invention the proper vector 130 that also can use more or less dimension.
Each unit that comprises the grid 320 of the pixel that comes from hand-written stroke is assigned to one of eight directions based on only actual stroke direction in this unit.The direction dimension of the unit of grid 320 is added to produce and does not compress input character proper vector 130.An octuple direction character vector is defined as V={v
1, v
2, v
3, v
4, v
5, v
6, v
7, v
8, v wherein
iValue be the quantity of i direction dimension in the grid 320, wherein (1＜=i＜=8).The proper vector of describing the character 305 that is shown in Fig. 3 like this will be designated as following value: v
1=4; v
2=0; v
3=7, v
4=0; v
5=0; v
6=0; v
7=0; v
8=3.
Referring to Fig. 4, it is a process flow diagram of having summarized the method 400 that above-mentioned proper vector 130 is provided.In step 405, receive the presentation of information 300 of input character 305.In step 410, the presentation of information 300 of this input character 305 is standardized as a grid 315.In step 415, each unit of grid 315 further is subdivided into grid 320.In step 420, extract the direction character of each grid 320.Next, in step 425, for not compressing each dimension designated value of input character proper vector 130.
According to the present invention, the model Character mother plate comprises the assembly average of the proper vector of the numerous input samples that come from each character.The feature of each input sample is extracted according to the processing identical with said method 400.
Referring to Fig. 5, it is one of the explanation not compact model character feature vector 110 that utilizes all model characters in the character set and produce the process flow diagram of the method 500 of the code book 125 in the code book generator module 105.In step 505, provide not compact model character feature vector 110 as mentioned above.Compact model character feature vector 110 is not constructed the template of each model character.Next, in step 510, handle the not compact model character feature vector 110 that hives off according to for example known in the field hiving off.For example: can use such as the K-Means method or with the grouping method of the GLA algorithm of vector quantization.According to a preferred embodiment of the present invention, octuple not compact model character feature vector 110 be grouped into 256 different groups 115.Hive off can with, for example the Euclidean distance of vector between 110 be basic.In step 515, method 500 provides code book 125 for template compression module 120 then.Code book 125 comprises 256 groups' 115 vectorial center, the code of each group 115 between designated one 0 to 255.The number that depends on analyzed model character, thousands of not compact model character feature vector 110 can be compressed into 256 groups 115.According to another embodiment of the invention, using portable electric appts of the present invention, that have the minimum memory resource can use group 115 still less to remove to define code book 125.
Referring to Fig. 6, it shows the process flow diagram of the method 600 of the compression template 135 of utilizing template compression module 120 to produce the model character.In step 605, each not compact model character feature vector 110 compare with group 115 in the code book 125.Next, in step 610, use the group's 115 of preferably mating each proper vector 130 single Code Number to replace each not compact model character feature vector 110.In step 615, the compression template 135 of model character is provided to template matches unit 140.
Original, uncompressed model character feature vector 110 more than described be the compression template 135 that how to be converted into the model character.For example, consider relevant and alphabet or dictionary the k digitizing character that is used with the above embodiment of the present invention.Store a corresponding not memory module of compact model character feature vector 110 needs are comprised k * m octuple vector, wherein m is the sum that is used to define the grid 320 of each character 305.Yet with above-mentioned processing of the present invention, each eight saves not that compact model character feature vector 110 is reduced to the single compressed template that only needs the model of 1 byte memory character 135.Necessary storer is reduced to initial 1/8 of the size that requires.
Referring to Fig. 7, the process flow diagram of the method 700 of the final candidate characters 150 of providing of a character recognition algorithm that is used for being correlated with is provided for it.In step 220, Accounting Legend Code this in 125 each group 115 and each do not compress distance between the input character proper vector 130.As known in the art, vector distance can use following formula to calculate:
Wherein, A={a1, a2, a3, a4, a5, a6, a7, a8} and B={b1, b2, b3, b4, b5, b6, b7, b8}.Thereby the distance between A and the B equals the absolute value sum of the difference between each corresponding dimension.In step 710, in look-up table 145, provide distance.Size apart from look-up table 145 is m * 256, and wherein m still is used to define the sum of the grid 320 of each input character 305 again.Perhaps, in other words, m is the sum that does not compress input character proper vector 13 that is used to define each input character 305.
The remainder of the process flow diagram that Fig. 7 provided will be described with reference to the synoptic diagram of figure 8, and Fig. 8 has illustrated a compact model Character mother plate 805.Template shown in Figure 8 comprises 49 grids, can make 49 code index in code book 125 like this.The value of grid be designated as 5,10 ..., 15}.The index of first grid 810 is 5, so the distance between first grid feature of first grid 810 and first feature in compression input character template first grid 320 is Dis
15Dis
15Value can in look-up table 145, find.Similarly, the distance between each feature in other grids of compact model Character mother plate 805 and the individual features in compression input character template is determined.After the distance of all 49 grids is determined, calculate total distance (DIS) of compressing between input character template and the compact model Character mother plate 805 according to following formula:
DIS=Dis
15+ Dis
2 10+ ... ...+Dis
49 15 Formula 2
Refer again to Fig. 7, in step 225, the compression template 135 of system's 100 computation model characters and do not compress distance between the input character proper vector 130.These distances of classification in step 720.In step 230, system 100 provides preceding n candidate characters 150 at last.
Therefore the present invention is one and is used in improving one's methods of the compression hand-written character template of electronics character recognition in handling.The use of code book 125 can make multidimensional not compact model character feature vector 110 to be compressed into preferably only be a code book index of a byte.And the present invention can preferably use a simple lookup technology, by the distance between each feature in input character and model character relatively input character proper vector 130 and model Character mother plate mated.When using in conjunction with character recognition system, the present invention just can realize the high precision character recognition and reduce the expense of storer and processing simultaneously.Reduce storer and processing expenditure and make the present invention be particularly suitable for and mancarried electronic aid, for example mobile phone and PDA(Personal Digital Assistant) use together.
Above-mentioned describing in detail only provides a preferred example embodiment, but the scope that is not intended to limit the present invention, application or configuration.On the contrary, the those skilled in the art that are described as of preferred example embodiment provide the effective description that is used to realize the preferred example embodiment of the present invention.Be appreciated that under the situation that does not deviate from the illustrated the spirit and scope of the present invention of appended claim, can carry out various conversion the function and the configuration of element and step.
Claims (14)
1. a method of compressing the hand-written character template may further comprise the steps:
Generation comprises the code book of vector, and the center of the not compact model character feature vector group that is provided by the model Character mother plate is provided described vector;
Described not compact model character feature vector is compared with described code book; And
The compression template of character supplies a model.
2. method as claimed in claim 1 further may further comprise the steps:
Calculate the described group of compact model character feature vector not and do not compress distance between the input character proper vector; And
Provide preceding n candidate characters by calculating the described distance of not compressing between input character proper vector and the described model Character mother plate, wherein n is a constant.
3. method as claimed in claim 1, the wherein said step of not compressing the input character proper vector that provides comprises the step of extracting feature from a standardized input character template.
4. method as claimed in claim 1, wherein said code book comprise and are no more than 256 groups.
5. method as claimed in claim 1, the wherein said input character proper vector of not compressing is the octuple vector.
6. method as claimed in claim 1, wherein said sorting technique of step use by n candidate characters before calculating the described distance of not compressing between input character proper vector and the described model Character mother plate and providing provides described candidate characters.
7. method as claimed in claim 6, wherein said candidate characters provides in the step of character identifying method.
8. a system that is used to compress the hand-written character template comprises:
A code book generator module is used to produce a code book, and wherein said code book comprises the group's of the not compact model character feature vector that definition is provided by the model Character mother plate the vector at center; And
A template compression module is operably connected to described code book generator module, is used for relatively the supply a model compression template of character of the described code book of described not compact model character feature vector sum.
9. system as claimed in claim 8 further comprises a template matches module, is operably connected to described template compression module and is used for providing candidate characters by the distance of not compressing between input character proper vector and the described model Character mother plate.
10. system as claimed in claim 9, wherein said template matches module further comprises one apart from the look-up table generator.
11. system as claimed in claim 8, wherein said code book comprises and is no more than 256 groups.
12. system as claimed in claim 8, the wherein said input character proper vector of not compressing is the octuple vector.
13. system as claimed in claim 9, wherein said template matches module use sorting technique that described candidate characters is provided.
14., wherein in character recognition system, provide described candidate characters as the system of claim 13.
Priority Applications (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB031255736A CN1303563C (en) | 2003-09-29 | 2003-09-29 | Method and system for compressing hand-written character template |
| EP04784422A EP1668568A4 (en) | 2003-09-29 | 2004-09-17 | Method and system for compressing handwritten character templates |
| PCT/US2004/030554 WO2005034026A2 (en) | 2003-09-29 | 2004-09-17 | Method and system for compressing handwritten character templates |
| RU2006114780/09A RU2006114780A (en) | 2003-09-29 | 2004-09-17 | METHOD AND SYSTEM FOR COMPRESSING MANUAL SYMBOLS TEMPLATES |
| KR1020067006168A KR20060056408A (en) | 2003-09-29 | 2004-09-17 | Method and system for compressing handwritten character templates |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB031255736A CN1303563C (en) | 2003-09-29 | 2003-09-29 | Method and system for compressing hand-written character template |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1604124A true CN1604124A (en) | 2005-04-06 |
| CN1303563C CN1303563C (en) | 2007-03-07 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB031255736A Expired - Fee Related CN1303563C (en) | 2003-09-29 | 2003-09-29 | Method and system for compressing hand-written character template |
Country Status (5)
| Country | Link |
|---|---|
| EP (1) | EP1668568A4 (en) |
| KR (1) | KR20060056408A (en) |
| CN (1) | CN1303563C (en) |
| RU (1) | RU2006114780A (en) |
| WO (1) | WO2005034026A2 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100719942B1 (en) * | 2002-03-27 | 2007-05-18 | 노키아 코포레이션 | Pattern recognition |
| US8077994B2 (en) | 2008-06-06 | 2011-12-13 | Microsoft Corporation | Compression of MQDF classifier using flexible sub-vector grouping |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0481988A (en) * | 1990-07-24 | 1992-03-16 | Sharp Corp | Clustering system |
| US5812697A (en) * | 1994-06-10 | 1998-09-22 | Nippon Steel Corporation | Method and apparatus for recognizing hand-written characters using a weighting dictionary |
| US6104833A (en) * | 1996-01-09 | 2000-08-15 | Fujitsu Limited | Pattern recognizing apparatus and method |
| US6345119B1 (en) * | 1996-02-19 | 2002-02-05 | Fujitsu Limited | Handwritten character recognition apparatus and method using a clustering algorithm |
| CN1181662C (en) * | 2000-07-21 | 2004-12-22 | 中兴通讯股份有限公司 | Hand written character input method for mobile telephone and mobile phone with said function |
| CN1140864C (en) * | 2001-01-02 | 2004-03-03 | 无敌科技(西安)有限公司 | Hand writing input method for hand held data processor |
-
2003
- 2003-09-29 CN CNB031255736A patent/CN1303563C/en not_active Expired - Fee Related
-
2004
- 2004-09-17 WO PCT/US2004/030554 patent/WO2005034026A2/en active Application Filing
- 2004-09-17 EP EP04784422A patent/EP1668568A4/en not_active Withdrawn
- 2004-09-17 KR KR1020067006168A patent/KR20060056408A/en not_active Application Discontinuation
- 2004-09-17 RU RU2006114780/09A patent/RU2006114780A/en not_active Application Discontinuation
Also Published As
| Publication number | Publication date |
|---|---|
| WO2005034026A2 (en) | 2005-04-14 |
| CN1303563C (en) | 2007-03-07 |
| WO2005034026A3 (en) | 2005-06-02 |
| EP1668568A2 (en) | 2006-06-14 |
| EP1668568A4 (en) | 2012-02-08 |
| RU2006114780A (en) | 2007-11-10 |
| KR20060056408A (en) | 2006-05-24 |
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