A text classifying apparatus (100), an optical character recognition unit (1), a text classifying method (S220) and a program are provided for performing the classification of text. A segmentation unit (110) segments an image into a plurality of lines of text (401-412; 451-457; 501-504; 701-705) (S221). A selection unit (120) selects a line of text from the plurality of lines of text (S222-S223). An identification unit (130) identifies a sequence of classes corresponding to the selected line of text (S224). A recording unit (140) records, for the selected line of text, a global class corresponding to a class of the sequence of classes (S225-S226). A classification unit (150) classifies the image according to the global class, based on a confidence level of the global class (S227-S228).

An information processing apparatus includes: a determiner that determines an area including a handwritten figure from image data; a recognizer that recognizes a handwritten character from the handwritten figure; an acquirer that acquires a file name; and a file generator that generates a file with a file name based on a handwritten character when the recognizer recognizes the handwritten character based on the image data and generates a file with the file name acquired by the acquirer when the recognizer does not recognize a handwritten character.

Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An example apparatus for decoding receipts includes, vertex feature representation circuitry to extract features from optical-character-recognition (OCR) words, polar coordinate circuitry to: calculate polar coordinates of the OCR words based on respective ones of the extracted features, graph neural network circuitry to generate an adjacency matrix based on the extracted features, post-processing circuitry to traverse the adjacency matrix to generate cliques of OCR processed words, and output circuitry to generate lines of text based on the cliques of OCR processed words.

A system, method, and computer program product provides a way to separate connected or adhered adjacent characters of a digital image for license plate recognition. As a threshold processing, the method performs a recognition of character adhesion by obtaining character parameters using an image processor. The parameters include a horizontal max crossing and a ratio of width and height. A first rule-based module is used responsive to the character parameters to distinguish the adhered characters (character adhesions) that are easy to judge, leaving the uncertain part to a character adhesion classifier model for discrimination. Character adhesion data is obtained by data augmentation including the adding of a random distance between two single characters to create class like adhered characters. Then the character adhesion classifier model of single character and character adhesion data is trained. Any uncertain part can be distinguished by the trained character adhesion classifier model.

Systems and methods for classifying images (e.g., ads) are described. An image is accessed. Optical character recognition is performed on at least a first portion of the image. Image recognition is performed via a convolutional neural network on at least a second portion of the image. At least one class for the image is automatically identified, via a fully connected neural network, based on one or more predictions, each of the one or more predictions being based on both the optical character recognition and the image recognition. Finally, the at least one class identified for the image is output.

According to an embodiment, the information processing apparatus includes an image interface, an input interface, a communication interface, and a processor. The image interface is configured to acquire a display screen image from an input device for inputting a character string included in a captured image in which recognition of the character string according to a first algorithm fails. The processor is configured to search for the captured image corresponding to the display screen image, acquire the character string based on a result of character recognition processing of the searched for captured image according to a second algorithm, and input the character string to the input device.

A method, computer system, and a computer program product for optical character recognition training are provided. A text image and plain text labels for the text image may be received. The text image may include words. The plain text labels may include machine-encoded text corresponding to the words. Semantic feature vectors for the words, respectively, may be generated based on the plain text label. The text image, the plain text labels, and the semantic feature vectors may be input together into a machine learning model to train the machine learning model for optical character recognition. The plain text labels and the semantic feature vectors may be constraints for the training.

A method includes executing a Optical Character Recognition (OCR) preprocessor on training images to obtain OCR preprocessor output, executing an OCR engine on the OCR preprocessor output to obtain OCR engine output, and executing an approximator on the OCR preprocessor output to obtain approximator output. The method further includes iteratively adjusting the approximator to simulate the OCR engine using the OCR engine output and the approximator output, and generating OCR preprocessor losses using the approximator output and target labels. The method further includes iteratively adjusting the OCR preprocessor using the OCR preprocessor losses to obtain a customized OCR preprocessor.

A computer-implemented method, a device, and a non-transitory computer-readable storage medium of automatically determining an interactive GUI element in a graphic user interface (GUI) to be interacted. The method includes: detecting, by the processor, one or more candidate interactive GUI elements in the GUI based on a plurality of algorithms; determining, by the processor, a likelihood indicator for each of the one or more candidate interactive GUI elements, a likelihood indicator indicating the likelihood that a candidate interactive GUI element associated with the likelihood indicator is an interactive GUI element to be interacted; and determining, by the processor, an interactive GUI element to be interacted from the one or more candidate interactive GUI elements based on the likelihood indicators.

Systems and methods for defending against emoji domain web address phishing are disclosed. The present techniques thus improve computer system security in various instances in which a web address includes an emoji character, which can be rendered as a graphical icon on a user system, and which may cause user confusion leading to a possible computer security breach. In an embodiment, a system receives a web address as an input to a web browser. The system processes the received web address to remove any emoji characters present in the received web address. The system compares the processed web address to the received web address and determines whether the processed web address matches the received web address. Various actions are performed to prevent successful phishing attempts against users based on whether the processed web address matches the received web address.