Abstract: The present application provides a method of training a text recognition model. The method includes: inputting a first sample image into the visual feature extraction sub-model to obtain a first visual feature and a first predicted text, the first sample image contains a text and a tag indicating a first actual text; obtaining, by using the semantic feature extraction sub-model, a first semantic feature based on the first predicted text; obtaining, by using the sequence sub-model, a second predicted text based on the first visual feature and the first semantic feature; and training the text recognition model based on the first predicted text, the second predicted text and the first actual text. The present disclosure further provides a method of recognizing a text, an electronic device, and a storage medium.

Abstract: Embodiments of the present invention are directed to document processing, and more particularly to systems and methods that can utilize relative positions between the content of the document and a decodable indicia affixed to the document. In one embodiment, indicia reading terminals are provided that include an imaging module for capturing a frame of image data of a document. The document can include one or more decodable indicia such as a form barcode and various content fields, which delineate particular content of the document. The form barcode can include information respecting the form design and form design data. This information can be used to process the content of the document such as by providing coordinates or similar location and positioning metrics for use in processing the content of the document.

Abstract: Embodiments of the present invention are directed to document processing, and more particularly to systems and methods that can utilize relative positions between the content of the document and a decodable indicia affixed to the document. In one embodiment, indicia reading terminals are provided that include an imaging module for capturing a frame of image data of a document. The document can include one or more decodable indicia such as a form barcode and various content fields, which delineate particular content of the document. The form barcode can include information respecting the form design and form design data. This information can be used to process the content of the document such as by providing coordinates or similar location and positioning metrics for use in processing the content of the document.

Abstract: Methods to improve the accuracy of non-contact measurements of an object's dimensions using a dimensioning system are disclosed. The methods include a method for creating a mathematical model (i.e., error model) based on an observed correlation between errors in an estimated dimension and the characteristics of the measurement used to obtain the estimated dimension. These error models may be created for various dimensions and stored for future use. The methods also include a method for using the stored error models to reduce the error associated with a particular dimensioning-system measurement. Here an error model is used to create an estimated error. The estimated error is then removed from the estimate of the dimension to produce a final estimate of the dimension that is more accurate.

Abstract: Methods to improve the accuracy of non-contact measurements of an object's dimensions using a dimensioning system are disclosed. The methods include a method for creating a mathematical model (i.e., error model) based on an observed correlation between errors in an estimated dimension and the characteristics of the measurement used to obtain the estimated dimension. These error models may be created for various dimensions and stored for future use. The methods also include a method for using the stored error models to reduce the error associated with a particular dimensioning-system measurement. Here an error model is used to create an estimated error. The estimated error is then removed from the estimate of the dimension to produce a final estimate of the dimension that is more accurate.

Abstract: Methods to improve the accuracy of non-contact measurements of an object's dimensions using a dimensioning system are disclosed. The methods include a method for creating a mathematical model (i.e., error model) based on an observed correlation between errors in an estimated dimension and the characteristics of the measurement used to obtain the estimated dimension. These error models may be created for various dimensions and stored for future use. The methods also include a method for using the stored error models to reduce the error associated with a particular dimensioning-system measurement. Here an error model is used to create an estimated error. The estimated error is then removed from the estimate of the dimension to produce a final estimate of the dimension that is more accurate.

Abstract: A universal type mounting structure for a car lamp bulb and a method for assembling the universal type mounting structure for a car lamp bulb with a bulb. The universal type mounting structure for a car lamp bulb is used for assembling the bulb by being cooperated with a bulb base and includes a contact piece, a reflecting mirror mounting part and a supporting seat, wherein the supporting seat is arranged in the reflecting mirror mounting part. According to the universal type mounting structure for a car lamp bulb, the assembly of arranged parts and the bulb is completed according to assembly procedures, so that high mounting convenience and dismounting convenience (single-hand mounting can be performed) are guaranteed.

Abstract: Dimensioning systems may automate or assist with determining the physical dimensions of an object without the need for a manual measurement. A dimensioning system may project a light pattern onto the object, capture an image of the reflected pattern, and observe changes in the imaged pattern to obtain a range image, which contains 3D information corresponding to the object. Then, using the range image, the dimensioning system may calculate the dimensions of the object. In some cases, a single range image does not contain 3D data sufficient for dimensioning the object. To mitigate or solve this problem, the present invention embraces capturing a plurality of range images from different perspectives, and then combining the range images (e.g., using image-stitching) to form a composite range-image, which can be used to determine the object's dimensions.

Abstract: A universal type mounting structure for a car lamp bulb and a method for assembling the universal type mounting structure for a car lamp bulb with a bulb. The universal type mounting structure for a car lamp bulb is used for assembling the bulb by being cooperated with a bulb base and includes a contact piece, a reflecting mirror mounting part and a supporting seat, wherein the supporting seat is arranged in the reflecting mirror mounting part. According to the universal type mounting structure for a car lamp bulb, the assembly of arranged parts and the bulb is completed according to assembly procedures, so that high mounting convenience and dismounting convenience (single-hand mounting can be performed) are guaranteed.

Abstract: Embodiments of an image reader and/or methods of operating an image reader can capture an image, identify a bar code or IBI form within the captured image, and, store or display the captured image responsive to an orientation of the bar code.

Abstract: A dimensioning system including a computing device running an alignment software program is disclosed. The alignment software uses range information from a range sensor in order to generate alignment messages. The alignment messages may help a user define a frame of reference and align the dimensioning system's range sensor for improved dimensioning performance.

Abstract: A method is presented for correcting errors in a 3D scanner. Measurement errors in the 3D scanner are determined by scanning each of a plurality of calibration objects in each of a plurality of sectors in the 3D scanner's field of view. The calibration objects have a known height, a known width, and a known length. The measurements taken by the 3D scanner are compared to the known dimensions to derive a measurement error for each dimension in each sector. An estimated measurement error is calculated based on scans of each of the plurality of calibration objects. When scanning target objects in a given sector, the estimated measurement error for that sector is used to correct measurements obtained by the 3D scanner.

Abstract: A method is presented for correcting errors in a 3D scanner. Measurement errors in the 3D scanner are determined by scanning each of a plurality of calibration objects in each of a plurality of sectors in the 3D scanner's field of view. The calibration objects have a known height, a known width, and a known length. The measurements taken by the 3D scanner are compared to the known dimensions to derive a measurement error for each dimension in each sector. An estimated measurement error is calculated based on scans of each of the plurality of calibration objects. When scanning target objects in a given sector, the estimated measurement error for that sector is used to correct measurements obtained by the 3D scanner.

Abstract: Dimensioning systems may automate or assist with determining the physical dimensions of an object without the need for a manual measurement. A dimensioning system may project a light pattern onto the object, capture an image of the reflected pattern, and observe changes in the imaged pattern to obtain a range image, which contains 3D information corresponding to the object. Then, using the range image, the dimensioning system may calculate the dimensions of the object. In some cases, a single range image does not contain 3D data sufficient for dimensioning the object. To mitigate or solve this problem, the present invention embraces capturing a plurality of range images from different perspectives, and then combining the range images (e.g., using image-stitching) to form a composite range-image, which can be used to determine the object's dimensions.

Abstract: Dimensioning systems may automate or assist with determining the physical dimensions of an object without the need for a manual measurement. A dimensioning system may project a light pattern onto the object, capture an image of the reflected pattern, and observe changes in the imaged pattern to obtain a range image, which contains 3D information corresponding to the object. Then, using the range image, the dimensioning system may calculate the dimensions of the object. In some cases, a single range image does not contain 3D data sufficient for dimensioning the object. To mitigate or solve this problem, the present invention embraces capturing a plurality of range images from different perspectives, and then combining the range images (e.g., using image-stitching) to form a composite range-image, which can be used to determine the object's dimensions.

Abstract: Dimensioning systems may automate or assist with determining the physical dimensions of an object without the need for a manual measurement. A dimensioning system may project a light pattern onto the object, capture an image of the reflected pattern, and observe changes in the imaged pattern to obtain a range image, which contains 3D information corresponding to the object. Then, using the range image, the dimensioning system may calculate the dimensions of the object. In some cases, a single range image does not contain 3D data sufficient for dimensioning the object. To mitigate or solve this problem, the present invention embraces capturing a plurality of range images from different perspectives, and then combining the range images (e.g., using image-stitching) to form a composite range-image, which can be used to determine the object's dimensions.

Abstract: Embodiments of the present invention are directed to document processing, and more particularly to systems and methods that can utilize relative positions between the content of the document and a decodable indicia affixed to the document. In one embodiment, indicia reading terminals are provided that include an imaging module for capturing a frame of image data of a document. The document can include one or more decodable indicia such as a form barcode and various content fields, which delineate particular content of the document. The form barcode can include information respecting the form design and form design data. This information can be used to process the content of the document such as by providing coordinates or similar location and positioning metrics for use in processing the content of the document.

Abstract: A dimensioning system including a computing device running an alignment software program is disclosed. The alignment software uses range information from a range sensor in order to generate alignment messages. The alignment messages may help a user define a frame of reference and align the dimensioning system's range sensor for improved dimensioning performance.

Abstract: Embodiments of the present invention comprise an indicia reading terminal including operatively configured to interact with a storage module to store data, including location data, of a decodable indicia found in a captured document image. In one embodiment, the indicia reading terminal can be provided with one or more pre-stored information about the decodable indicia and/or the document. In another embodiment the indicia reading terminal can be provided with instructions and similarly operatively configured components that can identify information about the decodable indicia, store such information in a table, and utilize the tabulated data to process captured image data of subsequent documents.

Abstract: A method is presented for correcting errors in a 3D scanner. Measurement errors in the 3D scanner are determined by scanning each of a plurality of calibration objects in each of a plurality of sectors in the 3D scanner's field of view. The calibration objects have a known height, a known width, and a known length. The measurements taken by the 3D scanner are compared to the known dimensions to derive a measurement error for each dimension in each sector. An estimated measurement error is calculated based on scans of each of the plurality of calibration objects. When scanning target objects in a given sector, the estimated measurement error for that sector is used to correct measurements obtained by the 3D scanner.