69 results about "Intelligent character recognition" patented technology

A method and apparatus is disclosed for reading and decoding information extracted from a form. In the system of the present invention, packages are randomly placed on a conveyor belt, with their labels facing a two-camera subassembly. As the conveyor belt moves, the two-camera subassembly continuously takes images of the belt underneath the overhead camera. The design of the camera permits it to take a high resolution image of a non-singulated, unjustified package flow. A digital image of the packages within the field of view of the camera is then transferred to the processing system for analysis. The processing system identifies individual packages in the image, extracts them and then analyzes the information written on the package labels. The analysis process utilizes conventional Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR) techniques to evaluate the information written on the package label. Once the information is decoded, the system either accesses a database record associated with the decoded machine-readable code, or creates a new record. When an unknown word image is encountered, the field-specific recognition process is aided by use of lexicon information, optimized based on installation-specific or user-specific criteria. The lexicon information is continuously revised based on processed form information. In a preferred embodiment, verified destination addresses associated with a user are alphabetized or rank-ordered based on frequency of occurrence. It is only after the system determines that the originating user is not stored in the database does it resort to the ZIP+4 or similar database to verify a destination address.

A method for intelligent speech recognition for premature enunciators is disclosed. The method allows for speech recognition systems to adjust to commands of premature enunciators, who speak before the system is ready receive speech input. The method comprises activating the speech recognition system, informing the user that the speech recognition system is ready to receive speech input, and initiating a listening period. The method further comprises receiving input from a user before the system is ready to receive such input, identifying the user as a premature enunciator, and adjusting the system to allow for earlier detection of user speech input.

The invention discloses an automatic detection method for a Chinese character area of a shop sign in a natural scene. The automatic detection method comprises the following steps of: A, acquiring vectors V1 and V2 which reflect main pixel colors of a sampling region image; B, determining a background outline of an original image by using the vectors V1 and V2; C, dividing an HSV (Hue, Saturation and Value) color space into eight color spaces including black, white, red, yellow, green, cyan, blue and fuchsine; C, carrying out color separation on an original image to obtain eight color distribution binary images; carrying out Chinese character connected area analysis on the eight color distribution binary images to obtain eight text line binary images; F, filtering text lines, which do not conform to a Chinese character writing rule, in each text line binary image to obtain refined Chinese character areas; G, mixing the refined Chinese character areas of different colors to obtain a Chinese character area of the original image. By using the automatic detection method for the Chinese character area of the shop sign in the natural scene, the detection accuracy of the Chinese character area can be efficiently improved, the automatic detection method has significant importance for application of Chinese character recognition in the natural scene.

The invention, which belongs to the technical field of image processing, discloses a depth-learning-based Chinese character recognition system realizing method. Preprocessing, segmentation, identification, reorganization are carried out on a picture with characters to form a text, thereby realizing conversion from the picture to the text. The method comprises: (1), picture pretreatment is carried out; to be specific, graying, binarization, and inclination correction are carried out on an inputted original picture to obtain a regular picture; (2), picture segmentation is carried out; to be specific, segmentation is carried out on an inputted picture with lots of characters, the picture is segmented into rows and then each row is segmented into single character; (3), picture identification is carried out; to be specific, the segmented single character is identified and an identification module is invoked for each single character picture to obtain an identification character result; and (4), text reorganization is carried out; to be specific, the obtained single character identification results are combined according to a sequence, correction is carried out, and then an identification result text segment is produced. With the image processing technique, graying and binarization are completed; and the generalization ability and the anti-interference capability are high.

An electronic forms management system is provided. The electronic forms management system includes a publishing unit, a data input unit and a server. The publishing unit is adapted to publish the form templates to the server. The data input unit is connected to the server, and is adapted to receive input data. The server is adapted to process the published form templates and the input data. In addition, the server includes an Intelligent Character Recognition (ICR) framework which is being adapted to receive one or more ICR engines for recognizing the input data.

One embodiment of the present invention provides a system that facilitates displaying automatically recognized text, wherein the automatically recognized text includes optical character recognition (OCR) text or intelligent character recognition (ICR) text. During operation, the system receives a source image containing text from a user. Next, the system performs a character-recognition operation on the source image to produce recognized text. The system then locates a data field within the source image. Next, the system identifies a subset of the recognized text that corresponds with the data field. After that, the system displays the source image to the user. At the same time, the system displays the subset of the recognized text in close proximity to the data field in the source image to facilitate comparing the recognized text with the text contained in the source image.

The invention discloses an early child education system and a method based on Chinese character recognition. The system comprises a user login unit, a template unit, an image matching unit, an assessment unit and an output unit, wherein the user login unit is used for inputting personal information of the kid; the template unit is used for generating a corresponding Chinese character template according to the personal information of the kid in the user login unit; the image matching unit is used for being matched with the Chinese character template and finding a corresponding Chinese character related image; the assessment unit is used for building an assessment model through the Chinese character template in the template unit, and the assessment model is used for judging the Chinese character recognition correct rate by the kid; and the output unit is used for outputting an electronic learning report according to the kid Chinese character recognition degree in the assessment model. According to the early child education system provided by the invention, the kid character recognition effects are improved, the image corresponding to the Chinese character is changed into an image facilitating character recognition by the kid, and character recognition by the kid is more efficient; the information bearing amount is large enough through the image matching unit; and the character recognition condition by the kid is assessed via the assessment unit.

In an intelligent character recognition (ICR) method for recognizing hand-written text images using a long-short term memory (LSTM) recurrent neural network (RNN), text images are segmented into text line images, and the text lines images are pre-processed to normalize the line height and to equalize the word spacings in each text line. Both training images used to train the RNN network and test images containing text to be recognized by the trained RNN network are pre-processed to have identical heights and identical word spacings between words. This method improves character recognition accuracy.