An image processing method for recognising characters included in an image. A first character recognition unit performs recognition of a first group of characters corresponding to a first region of the image. A measuring unit calculates a confidence measure of the first group of characters. A determination unit determines whether further recognition is to be performed based on the confidence measure. A selection unit selects a second region of the image that includes the first region, if it is determined that further recognition is to be performed. A second character recognition unit performs further recognition of a second group of characters corresponding to the second region of the image.

A system is provided for a distributed server network with embedded image decoder as a chain code program runtime event. In particular, the system may comprise a distributed computing network comprising one or more decentralized nodes, each of which may store a separate copy of a distributed data register. The system may further comprise one or more specialized nodes which receive, assess, and analyze user input data, where the one or more specialized nodes may include a client identity node comprising an embedded image decoder which may be configured to analyze image portions of the user input data. Once the image data has been analyzed, the client identity node may convert the image data into a text format for storage within the distributed register.

Methods and apparatus for end-to-end document image quality assessment without having ground truth for characters. An image quality assessment device integrates end-to-end machine learning without using ground truth to automatically predict an overall quality scores for images of documents. The image quality assessment device augments the size of individual image patches extracted from the images based on the contents and size of the images to maintain sufficient size of training data without compromising the reliability of the predicted scores for the images.

An information processing device includes a processor configured to: acquire a read image; extract items from the acquired read image through character recognition, the items including a documentary item to be treated as a search condition according to the Electronic Books and Documents Preservation Act; and control a display of a screen indicating corresponding items for confirming the extracted items such that the documentary item is distinguishable from an other item, that is, an item other than the documentary item among the corresponding items.

An intelligent scanner device configured to facilitate improved accuracy for storage of scanned documentation. The scanner device may include an image processor, an identification engine, and a metadata engine. The scanner device may utilize artificial intelligence processes to generate metadata information to be stored with scanned documents.

Detection of textual phrases in a non-horizontal orientation at a scene is a target problem. This disclosure relates to a processor implemented method to detect a text from multimedia content captured at a scene. An input original image is processed by a trained model to obtain individual character with bounding box on the original image. The original image is positioned by a gradient to obtain a rotated image if number of detected characters is not equal to number of expected characters on the original image. At least one missing character bounding box on the original image and on the rotated image are estimated to construct a horizontal text image if number of detected characters is not equal to number of expected characters on the rotated image. At least one missing character in the estimated bounding box is detected by at least one text returned from an optical character reader.

A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

An image processing device comprising a processor configured to: determine presence or absence of a face image for each of a plurality of pieces of image data read from at least one image presenting medium, and decide a relationship for the plurality of pieces of image data, based on a result of the determination. The relationship is a front and back of the image presenting medium or a page order, and the processor is further configured to: decides that image data including a face image is on a front side of the image presenting medium or on a page before image data not including a face image.

A method that includes extracting image features of a document image, executing an optical character recognition (OCR) engine on the document image to obtain OCR output, and extracting OCR features from the OCR output. The method further includes executing an anomaly detection model using features including the OCR features and the image features to generate anomaly score, and presenting anomaly score.

Systems and methods for training machine learning models are disclosed. An example method includes receiving a plurality of first outputs and a ground truth value for each first output, each first output including an extracted string and a raw confidence score, determining, for each first output, an accuracy metric based at least in part on the extracted string and its corresponding ground truth value, for each extracted string: determining a similarity metric between the respective extracted string and each other extracted string of the plurality of first outputs, and determining a pseudo-accuracy based at least in part on the determined similarity metrics and the determined accuracy metrics, generating training data based at least in part on the determined pseudo-accuracies and the plurality of first outputs, and training the machine learning model, based on the training data, to predict pseudo-accuracies associated with subsequent outputs from a document extraction model.