CN106503712A - One kind is based on stroke density feature character recognition method - Google Patents

One kind is based on stroke density feature character recognition method Download PDF

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Publication number
CN106503712A
CN106503712A CN201611008154.0A CN201611008154A CN106503712A CN 106503712 A CN106503712 A CN 106503712A CN 201611008154 A CN201611008154 A CN 201611008154A CN 106503712 A CN106503712 A CN 106503712A
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CN
China
Prior art keywords
image
word
character recognition
recognition method
density feature
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Pending
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CN201611008154.0A
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Chinese (zh)
Inventor
何新霞
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Guangxi University
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Guangxi University
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Priority to CN201611008154.0A priority Critical patent/CN106503712A/en
Publication of CN106503712A publication Critical patent/CN106503712A/en
Pending legal-status Critical Current

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Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/60Type of objects
    • G06V20/62Text, e.g. of license plates, overlay texts or captions on TV images
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/20Image preprocessing
    • G06V10/24Aligning, centring, orientation detection or correction of the image
    • G06V10/243Aligning, centring, orientation detection or correction of the image by compensating for image skew or non-uniform image deformations
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10Character recognition
    • G06V30/14Image acquisition
    • G06V30/148Segmentation of character regions
    • G06V30/153Segmentation of character regions using recognition of characters or words
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10Character recognition

Abstract

The present invention relates to image identification technical field, is based on stroke density feature character recognition method more particularly, to a kind of, including:Obtain images to be recognized;To obtaining Image semantic classification:Image slant correction is processed with correction chart picture and thresholding and obtains the single image of foreground information and background information;Analyzing and processing image:The textural characteristics in the ranks of analysis of the image, obtain the word matrix parameter of image;Segmentation figure picture:Image is cut based on the word matrix parameter, form several subimages, obtain the word block of image;Identification:Individual processing is carried out to word block, obtains the characteristics of image of word block, and described image feature is identified.The method word is simple, and discrimination is high.

Description

One kind is based on stroke density feature character recognition method
【Technical field】
The present invention relates to image identification technical field, a kind of more particularly, to a kind of computer glitch detecting system and method It is based on stroke density feature character recognition method.
【Background technology】
With the extensive application of the image acquisition equipments such as digital camera, photographic head, ultrahigh speed scanner, in image, information is got over To get over the concern for causing people.A kind of important expression way that word in the picture be image, semantic content, energy are wherein embedded in Important information required for some people is enough provided.Such as word in image can be the Description of content of the image, if energy Enough automatically extract and recognize the word in image, it is possible to allow computer automatic understanding picture material.Computer is allowed as the mankind one There is extremely important meaning for word in sample identification image, storage, classification, understanding and retrieval for image and video etc. Justice, it are mainly used in the high-tech sectors such as Chinese information processing, office automation and its translation, artificial intelligence, have wide General application prospect and commercial value.At present to image in word typically just carried out by the process of simple image segmentation Identification, it is impossible to carry out Automatic adjusument according to the character features in image, cause existing image character recognition method precision Relatively low, it is impossible to meet the demand of practical application.
【Content of the invention】
In view of the foregoing, it is necessary to a kind of computer glitch detecting system is provided and method is a kind of special based on stroke density Levy character recognition method, it is therefore intended that solve the existing image character recognition method technology relatively low to the accuracy of identification of word and ask Topic.
The purpose of the present invention is achieved through the following technical solutions:
One kind is comprised the following steps based on stroke density feature character recognition method:
Obtain images to be recognized;
To obtaining Image semantic classification:Image slant correction is processed with correction chart picture and thresholding and obtains foreground information and the back of the body The single image of scape information;
Analyzing and processing image:The textural characteristics in the ranks of analysis of the image, obtain the word matrix parameter of image;
Segmentation figure picture:Image is cut based on the word matrix parameter, form several subimages, obtain image Word block;
Identification:Individual processing is carried out to word block, obtains the characteristics of image of word block, and described image feature is entered Row identification;Described image characteristic-acquisition method is:Word block frame is calculated, in the word block p × q dot matrix for adding frame, Respectively to level, vertical, 45 degree and 135 degree of direction projections, each direction takes n value as feature, forms 4n dimensional feature vectors.
Further, described also include to recognizing that image carries out image noise reduction lifting knowledge to obtaining Image semantic classification The degree of accuracy of other places reason.
Further, described image noise reduction process can adopt Wavelet-denoising Method, morphology scratch filter method, intermediate value filter The methods such as ripple device method, adaptive wiener filter method and mean filter method.
Further, the thresholding is processed includes fixed threshold method, self-adaption thresholding method, Da-Jin algorithm or changes Dai Fa.
Further, image in image array is divided into the font in the matrix coordinate by image with the first pixel value table Show, background is represented with the second pixel value that the number of every the second pixel value of row in the matrix coordinate of statistical picture obtains an array; Statistics to some row high parameters, average statistics by parameter, acquisition font size parameter.
Further, described identification based on default clustering algorithm to cutting after word sub-block carry out at image segmentation Reason, obtains the Word message in word block, and is compared in preset system literal storehouse according to the Word message, root According to the word compared in structure determination image.
Further, the analyzing and processing image also includes carrying out expansion process to word block.
Further, the identification step includes that extracted word block is identified after being normalized again.
Beneficial effect of the present invention:The present invention is analyzed by the high textural characteristics of the row matrix for recognizing image, calculates figure As the matrix parameter of word, then character script size parameter is estimated based on the related matrix parameter of word, then to each Individual word is split soon, and word sub-block is identified, and improves the accuracy of cutting word sub-block, so as to improve word The precision of identification.
【Specific embodiment】
One kind is based on stroke density feature character recognition method, it is characterised in that comprise the following steps:
Obtain images to be recognized;Images to be recognized can be any image for needing to carry out Text region, images to be recognized Can come from external equipment.Images to be recognized can be original image, or obtain after carrying out pretreatment to original image Image, image to be identified can be the picture formats such as jpg, bmp, png.
To obtaining Image semantic classification, including thresholding process, thresholding process and slant correction.Thresholding process:Institute Stating thresholding and processing includes fixed threshold method, self-adaption thresholding method, Da-Jin algorithm or iterative method.The thresholding of image has Beneficial to the further process of image, the single image of foreground information and background information is obtained, makes image become simple, and data Amount reduces, and can highlight the profile of target interested.Thresholding process:Set as the quality of images to be recognized is limited to input Standby, environment and the printing quality of document, in image, printed character is identified before processing, needs according to noise Feature carries out denoising to images to be recognized, lifts the degree of accuracy of identifying processing, and image noise reduction process can be gone using small echo Make an uproar the methods such as method, morphology scratch filter method, median filter method, adaptive wiener filter method and mean filter method. Slant correction:As scanning and shooting process are related to artificial operation, the images to be recognized for being input into computer more or less can all be deposited In some inclinations, in image, printed character is identified before processing, it is necessary to carry out image direction detection, and correction chart Image space to.
Analyzing and processing image, the textural characteristics in the ranks of analysis of the image obtain the word matrix parameter of image;By image array The font that middle image is divided in the matrix coordinate by image represents that with the first pixel value background is represented with the second pixel value, statistics The number of every the second pixel value of row in the matrix coordinate of image, obtains an array;To some row high parameters, parameter is averaged statistics Data-Statistics, obtain font size parameter.
Segmentation figure picture:Image is cut based on the word matrix parameter, form several subimages, obtain image Word block;Word in also including to image before image cutting is carried out in character area carries out judgement orientation, can To word block scanning element line by line, to obtain the line space and column pitch of word in word block, and calculate literal line Height variance and text line width variance.The height variance of the literal line is used for the concordance for reflecting literal line height, and The width variance of text column is used for the concordance for reflecting word column width.Then the height of comprehensive this article between word spacing and literal line Or the factor such as the concordance of the width of text line is come to judge the word be transversely arranged or longitudinal arrangement.For example, if line space It is more than column pitch, and literal line is highly consistent, then judges that word is transversely arranged in character area.If column pitch is more than in the ranks Away from, and word column width is consistent, then judge that word is longitudinal arrangement in character area.The cutting result of word block is carried out Revise, such as the word row or column after including false segmentation merges, or to English initial and the false segmentation of the second letter It is modified
Identification:Individual processing is carried out to word block, obtains the characteristics of image of word block, and described image feature is entered Row identification;Before word being extracted using the word block after printed page analysis and individual character slicing operation from character area, Expansion process can also be carried out to the word block, then retain word edge gradient using the word block, remove local back The interference of scape gradient, so as to from the character area by each Word Input out, and extracted word is normalized Process, will all words zoom to unified size, the feature for finally extracting each word is identified.Described image feature is obtained The method of taking is:Calculate word block frame, plus the word block p × q dot matrix of frame in, respectively to level, vertical, 45 degree and 135 degree of direction projections, each direction take n value as feature, form 4n dimensional feature vectors.
Upper described, only it is presently preferred embodiments of the present invention, not makees any pro forma restriction to the present invention, although The present invention is disclosed as above with preferred embodiment, but is not limited to the present invention, and any those skilled in the art are not taking off In the range of technical solution of the present invention, when the technology contents using the disclosure above make a little change or are modified to equivalent variations Equivalent embodiments, as long as being that the technical spirit according to the present invention is to above example without departing from technical solution of the present invention content Any brief introduction modification, equivalent variations and the modification that is made, still falls within the scope of technical solution of the present invention.

Claims (8)

1. a kind of based on stroke density feature character recognition method, it is characterised in that to comprise the following steps:
Obtain images to be recognized;
To obtaining Image semantic classification:With correction chart picture, thresholding process obtains foreground information and background letter to image slant correction The image of interest statement one;
Analyzing and processing image:The textural characteristics in the ranks of analysis of the image, obtain the word matrix parameter of image;
Segmentation figure picture:Image is cut based on the word matrix parameter, form several subimages, obtain the text of image Word block;
Identification:Individual processing is carried out to word block, obtains the characteristics of image of word block, and described image feature is known Not;Described image characteristic-acquisition method is:Word block frame is calculated, in the word block p × q dot matrix for adding frame, respectively To level, vertical, 45 degree and 135 degree of direction projections, each direction takes n value as feature, forms 4n dimensional feature vectors.
2. stroke density feature character recognition method is based on according to claim 1, it is characterised in that:Described to obtaining figure As pretreatment is also included to recognizing that image carries out image noise reduction to lift the degree of accuracy of identifying processing.
3. stroke density feature character recognition method is based on according to claim 2, it is characterised in that:At described image noise reduction Reason can adopt Wavelet-denoising Method, morphology scratch filter method, median filter method, adaptive wiener filter method and average Filter method is carried out.
4. stroke density feature character recognition method is based on according to claim 1, it is characterised in that:The thresholding is processed Including fixed threshold method, self-adaption thresholding method and Da-Jin algorithm or iterative method.
5. stroke density feature character recognition method is based on according to claim 1, it is characterised in that:To scheme in image array Font as being divided in the matrix coordinate by image represents that with the first pixel value background is represented with the second pixel value, statistical picture The number of every the second pixel value of row in matrix coordinate, obtains an array;To some row high parameters, parameter takes average primary system to statistics Meter, obtains font size parameter.
6. stroke density feature character recognition method is based on according to claim 1, it is characterised in that:The identification is based on pre- If clustering algorithm to cutting after word sub-block carry out image segmentation process, obtain the Word message in word block, and root Compare in preset system literal storehouse according to the Word message, according to the word compared in structure determination image.
7. stroke density feature character recognition method is based on according to claim 1, it is characterised in that:The analyzing and processing figure As also including carrying out expansion process to word block.
8. stroke density feature character recognition method is based on according to claim 1, it is characterised in that:The identification step bag Include after extracted word block is normalized and be identified again.
CN201611008154.0A 2016-11-16 2016-11-16 One kind is based on stroke density feature character recognition method Pending CN106503712A (en)

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Citations (5)

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CN104050450A (en) * 2014-06-16 2014-09-17 西安通瑞新材料开发有限公司 Vehicle license plate recognition method based on video
CN104298982A (en) * 2013-07-16 2015-01-21 深圳市腾讯计算机系统有限公司 Text recognition method and device
CN105631486A (en) * 2014-10-27 2016-06-01 深圳Tcl数字技术有限公司 Method and device for recognizing images and characters

Patent Citations (5)

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Publication number Priority date Publication date Assignee Title
CN101038626A (en) * 2007-04-25 2007-09-19 上海大学 Method and device for recognizing test paper score
CN104298982A (en) * 2013-07-16 2015-01-21 深圳市腾讯计算机系统有限公司 Text recognition method and device
CN103902981A (en) * 2014-04-02 2014-07-02 浙江师范大学 Method and system for identifying license plate characters based on character fusion features
CN104050450A (en) * 2014-06-16 2014-09-17 西安通瑞新材料开发有限公司 Vehicle license plate recognition method based on video
CN105631486A (en) * 2014-10-27 2016-06-01 深圳Tcl数字技术有限公司 Method and device for recognizing images and characters

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