55 results about "Cursive" patented technology

The invention discloses a Chinese word stock automatic generation method based on writing style modeling, and the method comprises the steps: carrying out the stroke track automatic extraction and wrong result screening operation of inputted handwritten Chinese character image, and obtaining training data; carrying out the learning and modeling of handwritten Chinese character stroke shape and style and stroke inter-frame structure style through a neural network, and obtaining handwriting stroke connection characteristics and stroke end part contour handwriting features through statistical analysis, and then generating a high-quality Chinese character form consistent with the handwriting style of a user. The method just needs a few of commonly-used Chinese characters as the input, does not need any manual intervention, can automatically generate a vector Chinese word stock comprising a large number of Chinese character patterns, can quickly and automatically generates a handwriting Chinese word stock with other handwriting style for a user, can remarkably improves the manufacturing efficiency of the Chinese word stock, and greatly reduces the production cost.

The invention discloses a method for generating and beautifying handwritten characters with a computer. The method is realized by learning a certain amount of hand written character samples. The method comprises the following steps: decomposing and parameterizing the strokes of five to twenty different handwritten character samples for the same character, and converting each character into the form of sample vector by the image processing method; linear interpolation is conducted among characters to obtain new handwritten characters; evaluating each new character by an automatic computer evaluation method of character writing form aesthetic degree; and adjusting interpolation coefficients repeatedly through a nonlinear programming optimization method so as to achieve the effect of beautifying the character. The system of the invention can automatically generate handwritten characters with better aesthetic degree, and improve the aesthetic degree of handwritten characters of a user on the basis of reserving the basic characteristic of the handwriting of the user.

The invention discloses an off-line handwritten form Chinese character recognition method and belongs to the technical field of Chinese character recognition and extraction. The method comprises the steps of data collection, preprocessing, feature extraction and recognition extraction. A classification and recognition standard in the Chinese character recognition method comprises that a sample to be tested is classified according to a minimum distance standard, the feature recognition is performed based on a 4*4 elastic mesh, in rough classification recognition, the number of feature points such as end points, inflection points and cross points in each of meshes are conducted statistics on, and weight sum is respectively performed, in a second classification recognition, the number of transverse, vertical, left-falling and right-falling stroke vectors in each of the meshes is recorded, and handwritten form Chinese characters are recognized and extracted. According to the method, both off-line handwritten form Chinese characters and print form Chinese characters can be recognized, the feature extraction method of the Chinese character recognition method is simple, and the recognition speed is fast.

The invention relates to a method that can directly write in handwritten information, belonging to information input technology, which is characterized in that: no information identification and selection process is needed; the information is segmented or combined into a plurality of independent information units, and then the original handwritten handwriting is directly displayed on a display component; the operator writes in the handwriting information by an input member; the system captures the handwriting information write in by the operator and segments or combines the handwriting information into independent information units, and then the original handwriting is displayed on the display component. The invention has the advantages that the handwriting input by the method can further facilitate edit operation; the method is more suitable for the input of Chinese characters, special characters and formulas, and the information input efficiency is greatly improved; meanwhile the original handwriting of the writer is retained, and common informal documents can have more individualized characteristics.

The invention provides a rejection method for identifying handwritten character based on multiple classifiers, belonging to the field of handwritten character identification. The rejection method is led into a handwritten character identifying system, so as to effective improve the reliability of the identifying system and improve the identifying rate of the identifying system. The invention provides a rejection method based on a single classifier and the rejection method based on the multiple classifiers. Aiming at the limitation of a single characteristic rejection method, the idea of a multiple classifiers system is adopted to design the single classifier for the each characteristic, and integrate the rejection result of the each classifier, so as to play advantages of the each characteristic and improve the reliability of the rejection. Based on the rejection method of the single classifier, the invention provides the rejection method of the multiple classifiers. The method can better solve contradiction between the identifying rate and the rejecting rate of the handwritten character identifying system, and improve the reliability of the identifying system.

Input handwritten characters are classified as print or cursive based upon numerical feature values calculated from the shape of an input character. The feature values are applied to inputs of an artificial neural network which outputs a probability of the input character being print or cursive. If a character is classified as print, it is analyzed by a print character recognizer. If a character is classified as cursive, it is analyzed using a cursive character recognizer. The cursive character recognizer compares the input character to multiple prototype characters using a Dynamic Time Warping (DTW) algorithm.

The invention discloses a method and a system for generating a wrong answer list. The method comprises the following steps of: acquiring an electronic original test question picture, wherein the electronic original test question picture comprises printed characters and handwritten characters, and the colors of the printed characters are different from those of the handwritten characters and are consistent with those of characters in actual test paper; recognizing a color value of each pixel point in the electronic original test question picture; modifying colors of the pixel points of which the color values are in a preset range into background colors, and saving as a test question picture which does not contain the handwritten characters; and exporting the test question picture which does not contain the handwritten characters and the corresponding original test question picture to two word documents respectively to generate the wrong answer list, wherein the test question pictures in the two word documents are required to correspond one by one. Through the method and the system, the efficiency of generating the wrong answer list can be improved.

The invention provides a method for imitating handwriting of Chinese characters by using a computer. A computer image processing and artificial intelligent method can be adopted for learning and imitating personal handwriting with a computer. In the method, the available personal Chinese character samples in handwritten form are resolved in strokes and classified; each stroke, each radical, a character and the inner space structure of the character are parameterized in a unified way, and a personal handwritten character font database is established in combination with a plurality of ordinary types; when the personal handwriting is imitated, the Chinese character to be imitated is resolved into a radical or strokes, and each radical or stroke is found from the personal handwritten character font database one by one; the radical or the stroke is reconstituted according to the inner space structure features of the personal character as the output of the imitated handwriting. In the method, the stroke radical constitution rules of Chinese characters are analyzed directly; the characteristics of personal handwriting are seized more essentially, and personal handwriting is imitated better.

The hand writing distinguishing and inputting method in computer includes the following steps: image pre-treatment of the hand writing input from scanner; hand writing font extraction including line separation with the horizontal projection of text line and character separation with the vertical projection of text character; establishing template of computer font and hand written font including font characteristic vector extraction and classification; character matching including font characteristic extraction and matching in computer; and hand writing distinction via establishing the corresponding relation between hand writing font and computer font. The present invention is simple and convenient, and can facilitate man-machine conversation.

The invention discloses a character identification method and a character identification system. The character identification method comprises steps of inputting a to-be-identified area image and performing binary processing; extracting stroke characteristics of the to-be-identified area image; extracting relation characteristics between adjacent strokes of all strokes and the corresponding positions; matching radicals; matching characters; and outputting matching results of the to-be-identified area image. The character identification system comprises an image acquiring module, an image processing and character identifying module and a character outputting module. According to the invention, printed and quite neatly handwritten characters can be precisely identified; there are no other restrictions except that the handwritten characters could not be too illegible to read the structure characteristics; and a large number of printed and handwritten manuscripts, tables and bills can be processed at a high speed.

The invention discloses an offline handwritten mathematical formula recognition and reconstruction method, which is characterized in that a character segmentation problem is converted into a target detection problem in a formula segmentation stage, so that the segmentation problem of broken characters and adhesion characters is effectively solved, and the accuracy of a character segmentation result is greatly improved; at the character recognition phase, two convolutional neural network models based on batch normalization and global average pooling methods and only using convolution kernels with the size of 3 * 3 are designed for the problem of offline handwritten mathematical character recognition, the voting results of the two models are used as recognition results in the recognition stage, so that the recognition of mathematical characters can be accurately completed, and the character recognition accuracy is improved; at the formula analysis and reconstruction stage, the geometricconstraint is carried out according to the position relation among the characters, the semantic constraint is carried out by using a two-dimensional random context-independent grammar, and finally, the characters are combined from bottom to top, so that the formula reconstruction is completed.

The invention discloses a computer-assisted instruction method aiming at the writing of Chinese characters. By adopting a series of computer image processing and artificial intelligence, the method can realize automatic or semiautomatic computer-assisted instruction on the writing of Chinese characters. The method comprises the following steps: artificial scoring is performed to the features and the appearance ratings of a great quantity of hand-written character samples in advance; then the hand-written input of a writing user is received, and an automatic computer evaluation method for the appearance ratings of the writing shape of the characters is used for scoring the hand-written characters of the user; the hand-written characters of the user are parameterized and transformed to be expressed by the linear sum of a plurality of standard characters; and finally, a nonlinear programming optimization method is adopted, the interpolation coefficients are repeatedly adjusted to raise the score of the appearance ratings of the characters, and the user is guided to improve the appearance ratings of the characters on the basis of retaining the character features of the user; besides, the guidance can also be performed to only a part of a character or the overall structure of the character with the designation of the user.

The present invention relates to the Chinese character recognizing method based on structure model, and belongs to the fields of mode distinction, artificial intelligence and Chinese character information processing. The present invention constitutes two mathematic models, including stroke section center point model and stroke relational matrix model, for describing Chinese character structures and stroke section center point recognizing method and stroke relational matrix recognizing method with stroke section and stroke primitives, and the models. The Chinese characters are classified roughly in stroke section center point recognizing method and delicately in stroke relational matrix recognizing method, and the present invention forms one integral Chinese character recognizing method for both book letter and script Chinese characters with high correctness and stable performance.

The invention discloses a handwritten form keyword detection method comprising that: characteristic points of keyword images in a keyword image database are extracted so that a keyword characteristic database is established; the characteristic points of texts in text images to be detected are extracted so that a characteristic point database of the text images to be detected is obtained; sliding windows of the text images to be detected are extracted, characteristic point sets corresponding to the sliding windows are extracted from the characteristic point database, and the characteristic point sets are compared so that initial matching point pair sets are obtained; the initial matching point pair sets are screened so that accurate matching point pairs are obtained; and the sliding windows in the text images to be detected are screened according to the matching point pairs and integrated so that detection results are obtained. In the process of characteristic extraction, SIFT characteristic representation is adopted. The method is applicable to detection of keywords of large amounts of handwritten form documents, such as historical literatures, correspondences, notes, etc. Under the premise of establishing a black list image database, the keywords in a black list can be effectively detected and handwriting of different authors can also be distinguished.

A progressive handwriting tool utilizes one of (a) color contrast within a writing segment, or (b) a grid of sloped lines, to properly guide a student in writing print and/or cursive letters in a proper style and form. In one aspect, a print writing tool includes a writing medium, such as a sheet of paper or an electronic input capturing device, where a plurality of writing segments comprised of first and second rectangular regions are formed thereon. The rectangular regions extend horizontally in a stacked arrangement, and have contrasting colors to clearly differentiate between upper and lower areas where uppercase and lowercase letters are to be written. In another aspect, a cursive writing tool includes a writing medium onto which is formed a set of parallel horizontal lines and a set of diagonal lines of constant slope. The horizontal and diagonal lines intersect to form an array of writing boxes, with each box configured to have one letter of a cursive word written therein. The progressive handwriting tool may be accompanied by writing programs or instructions on how to practice handwriting skills with the tool, such that the programs and tool together form an instructional writing kit.

The invention discloses an English handwriting identification method based on improved VGG-16 model which comprises the following steps of collecting English document data sets from different handwriting of different persons; carrying out word segmentation on the obtained handwritten English handwriting document to obtain a data set composed of English words, and constructing a training sample setand a test sample set based on the data set; obtaining an improved VGG-16 model used for constructing a convolutional neural network, wherein the model comprises two traditional convolutional layers,three composite convolutional layers, five pooling layers and three full connection layers; inputting a training sample set, extracting character features, and carrying out classification training; and automatically identifying the English handwriting by using the trained neural network. The identification of English handwriting can be efficiently realized and identification accuracy rate can reach 100%. An algorithm has certain robustness on illumination change, simple geometric deformation and additional noise, and can be used in related fields of handwriting English handwriting identification. The algorithm can also be used for handwriting identification of other character handwritings after being expanded.

The invention provides a handwritten numeral recognition method and a handwritten numeral recognition device. The method comprises the following steps of: receiving a handwritten numeral image input by a user; extracting M covariance characteristics of the handwritten numeral image, wherein the value of M is any one positive integer which is greater than 1; and recognizing handwritten numerals according to the preset M covariance characteristics of each training image, a preset type tag carried by each training image and the M covariance characteristics of the handwritten numeral image. The handwritten numerals are recognized by extracting the M covariance characteristics of the handwritten numeral image, wherein the value of the M is any one positive integer which is greater than 1, so that the problem of inaccurate recognition of the handwritten numerals caused by adoption of a single covariance characteristic in the recognition process of the handwritten numerals in the prior art is solved.