Methods, apparatuses and computer program products for providing artificial-intelligence-based indicia data editing are provided. For example, an example computer-implemented method may include determining, based at least in part on a data processing model associated with a scan setting module, a first decoded data string corresponding to a first indicia; determining, based at least in part on user input data, a first input data string corresponding to the first indicia; generating a predictive indicia data editing model based at least in part on providing the first decoded data string and the first input data string to an artificial intelligence algorithm; and updating the scan setting module based at least in part on the predictive indicia data editing model.

A method of reading a tag located on an object, wherein the tag includes tag values associated with the object, where the method includes obtaining a first sensor output, by reading the tag using a tag reader, the first sensor output being at least one of an erroneous and incomplete representation of the tag values of the tag and includes deriving at least one tag value from the first sensor output using a model, where the model is based on a first set of sensor outputs generated by the tag reader by reading a plurality of tags and tag values associated with the plurality of tags, and where the first set of sensor outputs includes a plurality of sensor outputs, where each sensor output includes at least one of an erroneous and incomplete representation of at least one tag value of the corresponding tag.

To suppress an increase in processing time due to a load of inference processing while improving reading accuracy by the inference processing of machine learning. An optical information reading device includes a processor including: an inference processing part that inputs a code image to a neural network and executes inference processing of generating an ideal image corresponding to the code image; and a decoding processing part that executes first decoding processing of decoding the code image and second decoding processing of decoding the ideal image generated by the inference processing part. The processor executes the inference processing and the first decoding processing in parallel, and executes the second decoding processing after completion of the inference processing.

Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.

A method for processing an encoded pattern, a storage medium, and an electronic device are disclosed in this application. An electronic device acquires a first encoded pattern to be recognized, the first encoded pattern being a pattern obtained after predefined information is encoded therein. The electronic device increases resolution of the first encoded pattern through a target model to obtain a second encoded pattern, the target model being obtained through training by using a predetermined third encoded pattern as an input and a predetermined fourth encoded pattern as an output, the third encoded pattern being obtained by decreasing resolution of the fourth encoded pattern, the third and the first encoded patterns being encoded in the same manner. Finally, the electronic device decodes the second encoded pattern using a code recognition module to obtain the predefined information. This application resolves the technical problem that an encoded pattern cannot be accurately recognized.

A method for processing an encoded pattern, a storage medium, and an electronic device are disclosed in this application. An electronic device acquires a first encoded pattern to be recognized, the first encoded pattern being a pattern including predefined information encoded therein. The electronic device increases resolution of the first encoded pattern through a target model to obtain a target encoded pattern having a target resolution and corrects an edge of a sub-pattern in the target encoded pattern to obtain a second encoded pattern. Finally, the electronic device decodes the second encoded pattern using a code recognition module to obtain the predefined information. This application resolves the technical problem that an encoded pattern cannot be accurately recognized.

A barcode reader configured to capture interleaved frame types optimized for vision capture and barcode capture are disclosed herein. An example barcode reader is configured to operate in a pre-determined repetitive pattern of capturing a first frame and capturing a second frame over a reading cycle having a fixed duration after a triggering event, wherein the first frame is captured over a first exposure period having a first duration, and the second frame is captured over a second exposure period having a second duration, and wherein the first frame is associated with a first brightness parameter, and the second frame is associated with a second brightness parameter.

A waste stream is analyzed and sorted to segregate different items for recycling. Certain features of the technology improve the accuracy with which waste stream items are diverted to collection repositories. Other features concern adaptation of neural networks in accordance with context information sensed from the waste. Still other features serve to automate and simplify maintenance of machine vision systems used in waste sorting. Yet other aspects of the technology concern marking 2D machine readable code data on items having complex surfaces (e.g., food containers with integral ribbing for structural strength or juice pooling), to mitigate issues that such surfaces can introduce in code reading. Still other aspects of the technology concern prioritizing certain blocks of conveyor belt imagery for analysis. Yet other aspects of the technology concern joint use of near infrared spectroscopy, artificial intelligence, digital watermarking, and/or other techniques, for waste sorting. A variety of further features and arrangements are also detailed.

The disclosure includes a fixed retail scanner including a data reader, comprising a main board including one or more processors including a system processor, one or more camera modules, and an artificial intelligence (AI). The system processor is configured to transmit image data received from the one or more camera modules responsive to one or more event triggers detected by the system processor, and wherein the AI accelerator is configured to perform analysis based on an AI engine local to the AI accelerator in response to the event trigger. A remote server may also be operably coupled to the fixed retail scanner through the multi-port network switch, the remote server having a remote AI engine stored therein, wherein the local AI engine within the fixed retail scanner is a simplified AI model relative to the remote AI engine within the remote server.

The present disclosure is concerned with the technical field of marking objects with optically readable codes and reading out (decoding) the codes. Subjects of the present disclosure are a method, a system and a computer program product for decoding optically readable codes introduced in surfaces of objects.