The invention is a method for omnidirectional recognition of recognizable characters in a captured two-dimensional image. An optical reader configured in accordance with the invention searches for pixel groupings in a starburst pattern, and subjects located pixel groupings to a preliminary edge crawling process which records the pixel position of the grouping's edge and records the count of edge pixels. If two similar-sized pixel groupings are located that are of sizes sufficient to potentially represent recognizable characters, then the reader launches “alignment rails” at pixel positions substantially parallel to a centerline connecting the center points of the two similarly sized groupings. A reader according to the invention searches for additional recognizable characters within the rail area, and subjects each located pixel grouping within the rail area to a shape-characterizing edge crawling process for developing data that characterizes the shape of a pixel grouping's edge. After adjusting the orientation representation of the shape-characterizing data the reader compares the developed shape-characterizing data to previously stored shape-characterizing data to determine the character represented by the grouping on the basis of the best fit data.

Method for omnidirectional processing of 2D images including recognizable characters

A pattern recognition system, particularly a handprint character recognition system in which electrical binary black/white "image" of one or more handprinted characters is formed and a plurality of centers of recognition (CORs) within said binary black/white images are selected as reference points for measuring the characteristic enclave of the black/white image immediately surrounding the CORs. A library of templates of said measurements around the CORs for a plurality of known exemplary character images is stored in a memory for comparison with corresponding measurements made around the CORs of images whose class is unknown to produce "template scores" proportional to the similarity of the enclaves of the known image to the enclaves measured by templates. The generic shape of a character is expressed as a "character equation" involving template scores developed on an unknown image, and each character equation is evaluated including comparing the values of such equations, and selecting the best value to determine the generic name of the unknown character.

Character and pattern recognition machine and method

A feature-based character recognition identification and confidence level are determined for an unknown symbol. If the confidence level is within an intermediate range, the feature-based identification is confirmed by matching the unknown character with a reference template corresponding to the feature-based identification. If the confidence level is below the intermediate range, template matching character recognition is substituted in place of the feature-based identification. If the template matching recognition identifies more than one symbol, corresponding templates from a second set of templates having thicker character strokes are employed to resolve the ambiguity.

A hybrid feature-based and template matching optical character recognition system

A method for segmenting and recognizing license plates, in accordance with the present invention includes capturing an image of a license plate and preprocessing the image to prepare the image for segmentation and recognition Forward splitting regions of the license plate image into suspected characters regions, the suspected character regions are recognized in the license plate image and a confidence score is provided based on a probability of a correct match If the suspected characters have a confidence score below a threshold value, backward merging is employed which merges adjacent suspected character regions The backward merged suspected character regions in the license plate are recognized If the backward merged suspected character regions have a confidence score below the threshold value, the backward merging and recognizing steps are repeated to improve the confidence score

Character segmentation method for vehicle license plate recognition

A method for entering data into a computer generated form including field areas of preselected height and width includes the steps of converting handwritten characters of arbitrary height which may be greater than the preselected height formed on the screen to computer generated characters and displaying the computer generated characters within a field area. Additionally, handwritten characters to be entered into several field areas are grouped, converted, and displayed in selected field areas.

System for entering handwritten data into computer generated forms

Pattern segmentation and recognition in which hand-written characters are transformed electrically into 2-dimensional image patterns, wherein if ambiguity exists in segmenting a unit pattern including a character from the image patterns, character recognition is not made compulsively, but a plurality of possible unit patterns are first established. Then, the various unit patterns are segmented, and each unit pattern is identified to be a partial pattern, linked patterns, etc., so that each character is recognized on a basis of total judgement, whereby ambiguity of segmentation is resolved.

System and method for segmentation and recognition of patterns

PURPOSE: To segment and recognize patterns of characters written in natural writing conditions, by building up plural hypotheses to send unit patterns to a recognizing part when ambiguity occurs and resolving ambiguity by the synthetic judgement of recognition results. CONSTITUTION: When separated pattern components are put in adjacent frames, a recognizing part 200 inputs two hypotheses 51 and 53 to recognize a character and outputs recognition results 52 and 54 for respective hypotheses. That is, the result for the first hypothesis is SP5 and RJ6. SP5 means a subpattern of 5, and RJ6 means that a candidate is 6 though the character is rejected (unreadable). The result for the second hypothesis is AC5 and AC6, and they means that numbers 5 and 6 are accepted. Cosequently, the second hypothesis is appropriate, and recognition results are numbers 5 and 6. In a pattern dictionary 100, a partial pattern dictionary 101, a dictionary 102 of patterns of characters brought into contact with partial patterns, and a dictionary 103 of contacting character patterns are included besides a normal pattern dictionary 104. COPYRIGHT: (C)1984,JPO&Japio

Pattern segmenting and recognizing system

PURPOSE: To correctly binarize the thin line adjacent to thick line, by changing the binary reference method of video signal with the binary output comparing the signal greater by a given amount than white level being the reference of video signal with the video signal. CONSTITUTION: The peak value of video signal and the slice level dropped with a given time constant are one input T 2 of the multiplexer 54 via the voltage followers 51, 52. Another input T 1 of the multiplexer 54 is the slice level VT 1 being the fixed value greater by a given amount than the white level being the reference of video signal. Further, video signal enters the comparator 53 via the voltage follower 51 and outputs the control signal S of the multiplexer 54 with another input VT 13 . The output of the multiplexer is the slice level T 1 or T 2 with this control signal S, which is compared with the video signal at the comparator 55 to be binary coding signal. COPYRIGHT: (C)1980,JPO&Japio

Hitoshi Komatsu

Papers

System and method for segmentation and recognition of patterns

Pattern segmenting and recognizing system

Binary coding system