CN1619583B - Hand writing identifying method and system - Google Patents
Hand writing identifying method and system Download PDFInfo
- Publication number
- CN1619583B CN1619583B CN 200310116550 CN200310116550A CN1619583B CN 1619583 B CN1619583 B CN 1619583B CN 200310116550 CN200310116550 CN 200310116550 CN 200310116550 A CN200310116550 A CN 200310116550A CN 1619583 B CN1619583 B CN 1619583B
- Authority
- CN
- China
- Prior art keywords
- character
- vector
- characters
- confidence score
- greaterequal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Lifetime
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 239000013598 vector Substances 0.000 claims abstract description 69
- 230000006870 function Effects 0.000 claims description 13
- 239000000284 extract Substances 0.000 claims description 12
- 230000008878 coupling Effects 0.000 claims description 7
- 238000010168 coupling process Methods 0.000 claims description 7
- 238000005859 coupling reaction Methods 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000003068 static effect Effects 0.000 description 3
- 210000003811 finger Anatomy 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 206010034719 Personality change Diseases 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 210000003813 thumb Anatomy 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Abstract
The present invention disclosed a method for identifying hand-written characters and its system. Said method includes the following steps: receiving hand-written inputted character written on the user's interface of electronic equipment; then extracting directional characteristic vector and stroke segment characteristic vector from the described inputted character, finally comparing the above-mentioned two vectors with model character so as to define matched candidate character.
Description
Technical field
The present invention relates generally to the handwriting recognition processing.The present invention is specially adapted to the hand-written ideographic character of (but needn't be limited to) identification.
Background technology
In decades, the computer Recognition of printed text is faced with worried technical barrier always.Many optical character identification (OCR) technology just just is being improved recently, can discern the text document that scans in pin-point accuracy ground.But such OCR technology generally only can be changed the text that typewriter or electronic printer are printed.The computer Recognition of hand-written character still is very difficult technologies challenge.
The existing method that is used to discern hand-written character generally includes high-resolution template, and except two physical dimensions of text writing, it also catches the time dimension relevant with the text writing operation.Usually use electronic tablet to create such template, when pen, contact pilotage or finger contact tablet, tablet can be noted the stroke of text.Subsequently, by using various mode identification technologys, will mate with the template of model character corresponding to the direction character vector of inputting characters by handwriting.The template of model character comprises the assembly average from the direction character vector of a large amount of input samples of each character.Ideally, use the writer of such electronic tablet can see the thing (such as by using the electronics " ink " on the graphic user interface) that he or she writes, so the writer can control the sharpness of text.
But, some handheld device such as mobile phone and PDA(Personal Digital Assistant) comprises very little touch pad, the user is one folded one written character thereon, and can not receive any feedback, for example do not receive from graphic user interface, demonstrate the feedback how equipment changes this handwriting input.Therefore, this is in " blind " write characters.Do not meet with an accident, write on character on such touch pad and be easy to comprise distortion on a large amount of character shapes, especially for more complicated ideographic character, such as many Chinese and Japanese character.Therefore, the accurate electronic recognition that writes on the character on such touch pad difficulty especially just.
Therefore, need a kind of improving one's methods and system of handwriting recognition that be used for, it can overcome a large amount of character shape distortion (such as writing the distortion that causes owing to blind on touch pad), and can discern required hand-written character.
Summary of the invention
According to an aspect of the present invention, the present invention is a kind of method of handwriting recognition, and it comprises the steps: to receive the expression of writing the handwriting characters on the user interface of electronic equipment; From described input character, extract the direction character vector; From described input character, extract stroke section proper vector; And by thereby described direction character vector sum described stroke section proper vector and model character are compared the candidate characters of determining coupling.
That this method also can comprise is level and smooth by carrying out, noise deletion and size normalization are handled described handwriting characters is carried out pretreated step.
This method also can comprise the step that described handwriting characters is presorted.
This method also can comprise by only thereby described direction character vector and described model character contrast being provided the candidate character list and the corresponding first confidence score (v thereof of a weak point
i 2) step.
The step of the candidate characters of definite coupling can comprise candidate character list and the corresponding first confidence score (v thereof with described weak point
i 2) step that compares with described stroke section proper vector.
This method can further comprise the step of the no order function D (S) that calculates described handwriting characters.
Can calculate no order function D (S) according to following formula:
S in the formula
i=(x
i, y
i) and s
j=(x
j, y
j), s wherein
iAnd s
jIt is the starting point of two hand-written strokes.
Candidate character list and the corresponding first confidence score (v thereof with described weak point
i 2) step that compares with described stroke section proper vector can comprise: the corresponding second confidence score (v is provided
i 3), and according to following formula with the described first confidence score (v
i 2) and the described second confidence score (v
i 3) merge:
θ
2And θ
3Can according to whether D (S)＜T (S) change, wherein T (S) is the thresholding of the definite stroke number decision of experience, and D (S) is the no order function that calculates according to following formula:
S in the formula
i=(x
i, y
i) and s
j=(x
j, y
j), s wherein
iAnd s
jIt is the starting point of two hand-written strokes.
The candidate characters of coupling can be to have maximum v
iThe character of value.
According to a further aspect in the invention, the present invention is a kind of system that is used for handwriting recognition, and it comprises: microprocessor; The ROM (read-only memory) (ROM) that effectively links to each other with described microprocessor; The programmable storage that effectively links to each other with described microprocessor; And the tablet that effectively links to each other with described microprocessor; Described microprocessor is carried out the code that is stored among the described ROM effectively, write the expression of the handwriting characters on described tablet with reception, from described input character, extract the direction character vector, from described input character, extract stroke section proper vector, by the described stroke section of described direction character vector sum proper vector and the model character that is stored in the described programmable storage are compared definite candidate characters that mates.
, noise deletion level and smooth by carrying out and size normalization are handled, and microprocessor can further carry out pre-service to described handwriting characters effectively.
Microprocessor can further be presorted to described handwriting characters effectively.
By only described direction character vector being compared with described model character, microprocessor can further provide the candidate character list and the corresponding first confidence score (v thereof of a weak point effectively
i 2).
Microprocessor can be further effectively with the candidate character list and the corresponding first confidence score (v thereof of described weak point
i 2) compare with described stroke section proper vector.
In this specification and claims book, term " comprises (comprises, comprising) " or similar term means comprising of nonexcludability, therefore, the method or the device that comprise a series of assemblies not only comprise and also can comprise other assembly that those are not clearly listed well by the assembly that these have illustrated.
Description of drawings
In order to make easy to understand of the present invention and to put into practice, preferred embodiment done shown in now will be in conjunction with the accompanying drawings and quote, the wherein similar similar assembly of label representative, in the accompanying drawings:
Fig. 1 is according to one embodiment of present invention, illustrates the process flow diagram of hand-written recognition method of the Matching Model character candidates of the expression of determining to mate best a handwriting characters;
Fig. 2 is the synoptic diagram of the stroke section feature of Chinese character;
Fig. 3 is an example of the Chinese character write according to the standard stroke rule;
Fig. 4 is according to the present invention, illustrates the synoptic diagram of the matching process that produces second group of confidence score;
Fig. 5 is according to embodiments of the invention, illustrates to determine weight factor θ
2And θ
3The process flow diagram of method;
Fig. 6 is the mobile phone synoptic diagram that illustrates the tablet that includes the expression that can write hand-written character thereon.
Embodiment
Referring to Fig. 1, this process flow diagram according to one embodiment of present invention, illustrate the hand-written recognition method 100 of determining the Matching Model character candidates, this Matching Model character candidates has been mated the expression of handwriting characters best.Method 100 starts from step 105, receives the expression of handwriting characters here.Carry out pre-service at step 110 pair input character, calculate in step 115 and be used to create input character, the no order function D (S) relevant with order of strokes.In step 120, input character is presorted, to create the first short tabulation of matching candidate character.From input character, extract the direction character vector in step 125 then.In step 130, direction character vector and the model character of tabulating corresponding to first weak point of matching candidate character are compared, with the second short tabulation of creating the matching candidate character and the first confidence score (v that is correlated with
i 2).In step 135, from input character, extract stroke section proper vector.Compare in the second short tabulation of step 140 then, to create second group of confidence score (v stroke section proper vector and matching candidate character
i 3).At last, in step 145, discern handwriting characters by determining a matching candidate character.By with the first confidence score (v
i 2) and the second confidence score (v
i 3) merge to determine the matching candidate character.
The further details of method 100 is provided below.Method 100 can be incorporated in the hand-hold electronic equipments such as PDA(Personal Digital Assistant) and mobile phone, so that hand-write recognition function to be provided.In step 105, can submit a written statement to a higher authority at the user interface of electronic equipment and write the expression of hand-written input character.In step 110, pre-service can be carried out smoothly input character, noise removing and size normalization.No order function D (S) will be described in the instructions of back.In step 120, each all comprises in the group of dozens of character by all characters in the character set at first being grouped into (for example) hundreds of, thereby input character is presorted.For some language,, have 10,000 characters of surpassing in the character set such as Chinese.Then, the training pattern character compares input character and each model character then to represent each group.Then, by the first short tabulation of forming candidate characters corresponding to the character in the group of optimum matching.
In step 125, by method known in the field, method 100 extracts the direction character vector from input character. for example, at first make the pixel of digitized input character be suitable for grid and the normalization of N*N in pre-treatment step 110, thereby make size and the model character that is used for model of creation character direction proper vector big or small identical of input character. then, each element of N*N grid is subdivided into even thinner grid, to analyze then to extract the direction character vector. an example of direction character vector is that 8 dimension direction character vector .8 tie up each dimension of direction character vectors corresponding to a stroke direction that is used to create input character. by circumference being divided into the five equilibriums of 45 degree, can create eight stroke direction. those skilled in the art will recognize that, according to the present invention, the direction character vector also can use dimension more or still less. then, optimum matching according to actual stroke direction in the element, distribute one of eight dimensions for each element comprised from the grid of the pixel of hand-written stroke. with the direction dimension summation of grid elements, be defined as V={v then to create direction character vector .8 dimension direction character vector
1, v
2, v
3, v
4, v
5, v
6, v
7, v
8, wherein, v
iValue be the counting of i direction dimension in the grid, in the formula (1≤i≤8).Then, the dimension of 8 in each element of N*N grid direction character vector is averaged.At last, obtain the direction character vector of the 8*N*N dimension of whole input character.
Model character feature vector comprises the assembly average from the proper vector of a plurality of input samples of each character.Model character feature vector comprises the proper vector of all characters in the character set.
Next, method 100 continues in step 130, here the direction character vector is compared with model character feature vector, to create the second short tabulation of candidate characters.The second short tabulation of candidate characters is the subclass of the first short tabulation of candidate characters.Then, based on the optimum matching comparison of distance between the direction character vector of the direction character of input character vector and candidate characters, with confidence score (v
i 2) distribute to the candidate characters in the second short tabulation.Distance between the vector for example can be based on Euclidean distance (Euclidean distance) or city block distance (city block distance).
Then, method 100 continues in step 135, extracts stroke section proper vector here from input character.Obtain stroke section proper vector by the third dimension degree (time dimension) that uses input character.Referring to Fig. 2, illustrate the stroke section feature of Chinese character.Writing stroke section between the local maximum deflection of input character changes has defined stroke section feature.Obtain then every section bivector (dx, dy), the coordinate distance between the starting and ending point of dx and the dy section of being wherein.According to the complexity of input character, the number that is used to define the stroke section proper vector of input character also can change.
Stroke section proper vector is used to determine whether that the mode to be similar to prediction writes input character.For example, Chinese character has standard stroke order, can observe the order of stroke usually when connecting written character.When the writing Chinese characters character, the standard stroke order is followed following rule usually: 1) the left back right side of elder generation; 2) under after in the elder generation.Referring to Fig. 3, show an example of the Chinese character of writing according to top standard stroke rule.As shown in Figure 3, standard stroke is " s1, s2, s3, s4 " in proper order, and wherein the stain in each is represented the starting point of stroke.Notice that s2 is below s1, s3 is on the right side of s2, s4 below s3, the right side of s3.
In step 140, method 100 continues, and here the above-mentioned stroke section proper vector that will obtain from input character compares with second short being listed as of matching candidate character, to create second group of confidence score (v
i 3).Referring to Fig. 4, illustrate and produce second group of confidence score (v
i 3) the synoptic diagram of comparison process.The stroke segment model sequence of the stroke that will be listed as corresponding to second weak point of matching candidate character and the stroke section proper vector of input character compare.Stroke segment model sequence comprises probability distribution function (PDF), such as Gaussian Mixture PDF (Gaussian mixture PDF).Can be inconsistent from the number of the proper vector of input character with number from the stroke segment model of matching candidate character.Therefore use dynamic programming (DP) that model sequence and input character are compared.Calculate confidence score (v
i 3) measure as of each contrast.During DP handled, the confidence score between stroke section proper vector and the segmented model was confirmed as the likelihood score of proper vector and segmented model coupling.
At last, in step 145, method 100 is passed through the first confidence score (v
i 2) and the second confidence score (v
i 3) merge and determine the best match candidate character, thereby the identification inputting characters by handwriting.Merge two groups of confidence scores according to following formula:
Choose and maximal value v
iThe candidate characters that is complementary is as the best match candidate character.θ
2And θ
3Be to be used for determining where to organize confidence score (v
i 2Or v
i 3) more may provide the weight factor of accurate recognition result.
Referring to Fig. 5, illustrate and determine θ
2And θ
3The process flow diagram of method 500 of value.At first, in step 505, method 500 determines that input character is to write to connect a plurality of independent strokes that also are to use of writing.Connect the most possible conformance with standard order of strokes of input character that pen is write; Therefore, if write input character to connect pen, method 500 is output valve θ immediately
2=a
1And θ
3=a
2, a wherein
1And a
2Rule of thumb determine by testing input character and model candidate characters to compare.In addition, if determine that in step 505 input character is to use a plurality of independent strokes to write, then method 500 proceeds to step 510, calculates no order function D (S) here.No order function D (S) is the order of strokes of input character and measuring of standard stroke sequence consensus degree.For example, in an embodiment of the present invention, for Chinese, D (S) can calculate as described below.
At first, definition d (s
i, s
j), it has described the relation between two strokes, wherein s
i=(x
i, y
i) and s
j=(x
j, y
j) be the starting point coordinate of two strokes.Then according to following formula definition d (s
i, s
j):
Notice that formula 2 meets as above the rule in conjunction with the qualification Chinese standard stroke order that Fig. 3 explained.Formula 2 is applied to Chinese character shown in Figure 3, can obtains d (s
1, s
2)=1, d (s
1, s
3)=0, d (s
1, s
4)=0, d (s
2, s
3)=1, d (s
2, s
4)=0, d (s
3, s
4)=0.
Unordered function definition the relation between all strokes of input character.Suppose S=s
1s
2... s
nBe one group of stroke sequence, D (S) can be defined as so:
For example, again referring to the Chinese character described in Fig. 3, if i.e. S=s write in proper order in this character according to its standard
1s
2s
3s
4, use formula 2 and 3 can calculate D (S)=2 so.If character is write according to reverse order, i.e. S=s
4s
3s
2s
1, can calculate D (S)=10 so.Table 1 has provided the example of other value of D (S) that is used to create the order of strokes of Chinese character " my god (Tian (1)) " according to other.
Table 1
| S | D(S) |
| s 4s 1s 2s 3 | 8 |
| s 1s 4s 3s 2 | 6 |
| s 3s 2s 4s 1 | 6 |
| s 2s 1s 3s 4 | 2 |
| ... | ... |
In general, the input character of writing according to the standard stroke order will have less D (S) value, and will have bigger D (S) value with the character that non-standard order is write.
Referring to Fig. 5, calculating no order function D (S) afterwards once more, method 500 proceeds to step 515, and here value and the threshold T (S) with D (S) compares.T (S) is based on the sample of the model character of writing according to different order of strokes and rule of thumb determines.For example, once more referring to Fig. 3, the thresholding of four-stroke character can be set to T=4, thus stroke sequence s
1s
2s
3s
4And s
2s
1s
3s
4Just can be accepted as the standard generic sequence, other sequence then is regarded as non-standard order.
If D (S)＜T (S) this means that input character may write according to the standard order, method 500 output valve θ once more so
2=a
1And θ
3=a
2But if D (S)＞T (S) this means that input character can not write according to the standard order, method 500 will be exported another class value θ so
2=b
1And θ
3=b
2
Referring to Fig. 6, illustrate the synoptic diagram of electronic equipment or system with form such as the mobile phone 601 that can be used for realizing said method of the present invention.Phone 601 comprises the radio frequency communications unit 602 that connects and communicate by letter with processor 603.User interface (in the present embodiment, in form being display screen 605, keypad 606 and tablet 619) also is connected and communicates by letter with processor 603.As the skilled person will be apparent, when display screen was touch-screen, tablet 619 can be the part of display screen 605.
Processor 603 comprises the encoder/decoder 611 with the ROM (read-only memory) (ROM) 612 that is associated, and ROM 612 is used to store by mobile phone 601 transmissions or the encoding and decoding voice that receive or the data of other signal.Processor 603 also comprises microprocessor 613, ROM (read-only memory) (ROM) 614, random-access memory (ram) 604, static programmable memory 616 and detachable sim module 618 that microprocessor 613 is connected to encoder/decoder 611 by conventional data and address bus 617 and is associated.Wherein, static programmable memory 616 and sim module 618 can memory model character feature vector sum use the expression and the out of Memory of the input character of tablet 619 inputs.
Radio frequency communications unit 602 is receiver and the transmitters with merging of common antenna 607.Communication unit 602 has the transceiver 608 that is connected to antenna 607 by radio frequency amplifier 609.Transceiver 608 is also connected to the modulator/demodulator 610 of merging, and the modulator/demodulator 610 of merging is connected to processor 603 with communication unit 602.
Therefore, the user of phone 601 can write one or more characters on tablet 619, and phone 601 will all be stored in these characters in random-access memory (ram) 604, static programmable memory 616 and/or the detachable sim module 618.Then, the user of phone 601 can issue an order (for example using keypad 606), requires identification to use the hand-written character of tablet 619 inputs.
The order of identification hand-written character can be handled by microprocessor 613.By using the code that stores among the ROM 612, microprocessor 613 will be carried out aforesaid method of the present invention subsequently, determine the matching candidate character by direction character vector sum stroke section proper vector and model character being compared for each input character.According to the requirement of special system, microprocessor 613 can be carried out further order based on the input character of identification subsequently.Further order like this comprises: the information of input character that for example sends the following a piece of news of the input character comprise identification or will comprise identification is in address book.
Therefore, the present invention is used to discern improving one's methods and system of the hand-written character write on the user interface of electronic equipment. and the present invention is especially effective such as ideographic characters such as Chinese or Japanese character for identification, contain many strokes of writing according to the standard order usually in these ideographic characters. by analyzing stroke section proper vector and the direction character vector that is associated with input character, the present invention has increased the possibility of correct identification input character.
Above detailed description only provided preferred example embodiment, and do not want to limit the scope of the invention, applicability or configuration.Detailed description to this preferred example embodiment provides the description that preferred example embodiment of the present invention can be put into practice to those skilled in the art.Should be appreciated that, under the prerequisite of the spirit and scope of the present invention that do not deviate from the claims to be set forth, can make various changes the function and the structure of assembly and step.
Claims (12)
1. the method for a handwriting recognition, it comprises:
The expression of the handwriting characters on the user interface of electronic equipment is write in reception;
From described input character, extract the direction character vector;
From described input character, extract stroke section proper vector; With
By thereby described direction character vector sum described stroke section proper vector and model character are compared the candidate characters of determining coupling; With
Thereby only provide the candidate character list of a weak point and the step of corresponding first confidence score thereof with described direction character vector and described model character contrast.
2. the method for claim 1, it comprises further that, noise deletion level and smooth by carrying out and size normalization are handled described handwriting characters is carried out pretreated step.
3. the method for claim 1, it further comprises the step that described handwriting characters is presorted.
4. the method for claim 1, the step of the candidate characters of wherein said definite coupling comprise the step that the candidate character list of described weak point and corresponding first confidence score thereof and described stroke section proper vector are compared.
5. the method for claim 1, its further comprise calculate described handwriting characters step, wherein calculate described no order function D (S) according to following formula:
S in the formula
i=(x
i, y
i) and s
j=(x
j, y
j), s wherein
iAnd s
jIt is the starting point of two hand-written strokes.
6. method as claimed in claim 4, wherein said candidate character list and the corresponding first confidence score (v thereof with described weak point
i 2) step that compares with described stroke section proper vector can comprise:
The corresponding second confidence score (v is provided
i 3); With
According to following formula with the described first confidence score (v
i 2) and the described second confidence score (v
i 3) merge:
7. method as claimed in claim 6, wherein θ
2And θ
3Whether change less than T (S) according to D (S), wherein, T (S), and D (S) is the no order function that calculates according to following formula if being the thresholding of the definite stroke number decision of experience:
S in the formula
i=(x
i, y
i) and s
j=(x
j, y
j), s wherein
iAnd s
jIt is the starting point of two hand-written strokes.
8. method as claimed in claim 7, the candidate characters of wherein said coupling are to have maximum v
iThe character of value.
9. system that is used for handwriting recognition, it comprises:
Microprocessor;
The ROM (read-only memory) (ROM) that operationally links to each other with described microprocessor;
The programmable storage that operationally links to each other with described microprocessor; With
The tablet that operationally links to each other with described microprocessor;
Described microprocessor is operationally carried out the code that is stored among the described ROM, write the expression of the handwriting characters on described tablet with reception, from described input character, extract the direction character vector, from described input character, extract stroke section proper vector, by the described stroke section of described direction character vector sum proper vector and the model character that is stored in the described programmable storage are compared definite candidate characters that mates, wherein microprocessor provides the candidate character list and corresponding first confidence score thereof of a weak point by only described direction character vector being compared with described model character.
10. system as claimed in claim 9, wherein, described processor is level and smooth by carrying out, noise is deleted and size normalization is handled, and further operationally described handwriting characters is carried out pre-service.
11. system as claimed in claim 9, wherein, described microprocessor is further operationally presorted to described handwriting characters.
12. system as claimed in claim 9, wherein, described microprocessor is further operationally with the candidate character list and the corresponding first confidence score (v thereof of described weak point
i 2) compare with described stroke section proper vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200310116550 CN1619583B (en) | 2003-11-20 | 2003-11-20 | Hand writing identifying method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200310116550 CN1619583B (en) | 2003-11-20 | 2003-11-20 | Hand writing identifying method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1619583A CN1619583A (en) | 2005-05-25 |
| CN1619583B true CN1619583B (en) | 2010-05-05 |
Family
ID=34760681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 200310116550 Expired - Lifetime CN1619583B (en) | 2003-11-20 | 2003-11-20 | Hand writing identifying method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN1619583B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100407117C (en) * | 2005-09-02 | 2008-07-30 | 摩托罗拉公司 | Inputting characters into electronic equipment |
| US7817857B2 (en) * | 2006-05-31 | 2010-10-19 | Microsoft Corporation | Combiner for improving handwriting recognition |
| CN101256624B (en) * | 2007-02-28 | 2012-10-10 | 微软公司 | Method and system for establishing HMM topological structure being suitable for recognizing hand-written East Asia character |
| JP5253788B2 (en) * | 2007-10-31 | 2013-07-31 | 富士通株式会社 | Image recognition apparatus, image recognition program, and image recognition method |
| CN101893960B (en) * | 2010-06-28 | 2011-12-21 | 北京捷通华声语音技术有限公司 | Direction vector-based character recognition method and device |
| CN102147681B (en) * | 2011-05-03 | 2013-03-13 | 惠州Tcl移动通信有限公司 | Input method and device based on touch screen |
| CN102622554A (en) * | 2011-12-19 | 2012-08-01 | 广东步步高电子工业有限公司 | Touch screen mobile device, and method and system for soft locking password setup and unlocking for touch screen mobile device |
| CN104915627B (en) * | 2014-03-11 | 2019-04-26 | 重庆邮电大学 | A kind of character recognition method and device |
| CN113139533B (en) * | 2021-04-06 | 2022-08-02 | 广州大学 | Method, device, medium and equipment for quickly recognizing handwriting vector |
| CN113011412A (en) * | 2021-04-15 | 2021-06-22 | 深圳市鹰硕云科技有限公司 | Character recognition method, device, equipment and storage medium based on stroke order and OCR (optical character recognition) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5303311A (en) * | 1990-03-12 | 1994-04-12 | International Business Machines Corporation | Method and apparatus for recognizing characters |
| CN1388947A (en) * | 2000-08-31 | 2003-01-01 | 惠普公司 | Character recognition system |
-
2003
- 2003-11-20 CN CN 200310116550 patent/CN1619583B/en not_active Expired - Lifetime
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5303311A (en) * | 1990-03-12 | 1994-04-12 | International Business Machines Corporation | Method and apparatus for recognizing characters |
| CN1388947A (en) * | 2000-08-31 | 2003-01-01 | 惠普公司 | Character recognition system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1619583A (en) | 2005-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102449640B (en) | Recognizing handwritten words | |
| US8768062B2 (en) | Online script independent recognition of handwritten sub-word units and words | |
| US7437001B2 (en) | Method and device for recognition of a handwritten pattern | |
| CN101484907B (en) | A method and apparatus for recognition of handwritten symbols | |
| US7580551B1 (en) | Method and apparatus for analyzing and/or comparing handwritten and/or biometric samples | |
| US7778464B2 (en) | Apparatus and method for searching for digital ink query | |
| US20040096105A1 (en) | Method, device and computer program for recognition of a handwritten character | |
| CN1619583B (en) | Hand writing identifying method and system | |
| CN103279770A (en) | Handwriting recognition method based on fragment and contour feature of stroke | |
| KR20100103351A (en) | Character recognition apparatus, character recognition program and character recognition method | |
| CA2601144C (en) | Method and system for adaptive recognition of distorted text in computer images | |
| US6339655B1 (en) | Handwriting recognition system using substroke analysis | |
| CN110414622B (en) | Classifier training method and device based on semi-supervised learning | |
| Kim et al. | Off-line recognition of handwritten Korean and alphanumeric characters using hidden Markov models | |
| Jana et al. | Optical character recognition from text image | |
| KR20060096208A (en) | Method for displaying script in character recognition system | |
| WO2006019537A1 (en) | Method and system for handwriting recognition using background pixels | |
| Adhvaryu | Optical character recognition using template matching (Alphabet & Numbers) | |
| Mahasukhon et al. | Hand-printed English character recognition based on fuzzy theory | |
| Konstantakis et al. | A writer identification system of greek historical documents using matlab | |
| Nagabhushan et al. | Recognition of Pitman shorthand text using tangent feature values at word level | |
| JP4335185B2 (en) | Character identification based on written information | |
| Kim et al. | Substroke matching by segmenting and merging for online Korean cursive character recognition | |
| CN115346222A (en) | Handwritten Chinese character quality evaluation model obtaining method, evaluation method and device | |
| Mazlan | Development of Images to Braille Conversion Software for Alphabets Using Template Matching and Rule Based |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| ASS | Succession or assignment of patent right |
Owner name: MOTOROLA MOBILE CO., LTD Free format text: FORMER OWNER: MOTOROLA INC. Effective date: 20110120 |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20110120 Address after: Illinois State Patentee after: MOTOROLA MOBILITY, Inc. Address before: Illinois, USA Patentee before: Motorola, Inc. |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| C56 | Change in the name or address of the patentee | ||
| CP01 | Change in the name or title of a patent holder |
Address after: Illinois State Patentee after: MOTOROLA MOBILITY LLC Address before: Illinois State Patentee before: MOTOROLA MOBILITY, Inc. |
|
| TR01 | Transfer of patent right |
Effective date of registration: 20160309 Address after: California, USA Patentee after: Google Technology Holdings LLC Address before: Illinois State Patentee before: MOTOROLA MOBILITY LLC |
|
| CX01 | Expiry of patent term |
Granted publication date: 20100505 |
|
| CX01 | Expiry of patent term |