A translation method includes acquiring an image, where the image includes a text to be translated; splitting the text to be translated in the image and acquiring a plurality of target objects, where each of the plurality of target objects includes a word or a phrase of the text to be translated; receiving an input operation for the plurality of target objects, acquiring an object to be translated among the plurality of target objects, and translating the object to be translated.

Embodiments disclosed herein provide for systems and methods of separating characters associated with ligatures in digitized documents. The systems and methods provide for a ligature detection engine configured to identify the ligatures, and a ligature processing engine configured to identify and remove the glyphs attaching the separate characters forming the ligature.

An image processing apparatus includes a character determining unit configured to divide the read image into multiple blocks, each of the multiple blocks including multiple characters, and determine an inclination of each of the multiple characters included in each of the multiple blocks, a block processing unit configured to detect a change point block, the change point block being a block including characters having an inclination included in a first inclination interval, a number of the characters being equal to or larger than a first threshold, and including characters having an inclination included in a second inclination interval, a number of the characters being equal to or larger than the first threshold, the second inclination interval being different from the first inclination interval, and a fold determining unit configured to determine that the document is folded if the change point block is detected.

An information processing method and apparatus are provided that performs operations including identifying, from an image obtained via an image capture device, at least one character string that is relevant in identifying information to be extracted from the image; defining an area, within the image, that includes information as an information extraction area, the information including a plurality of information elements; selecting a region within the defined area where the information to be extracted is expected to be present using a feature within the defined area; removing the feature from the selected region and correcting one or more errors associated with the information caused by the removal of the feature and extracting one or more alphanumeric characters from the corrected information, wherein the extracted one or more alphanumeric characters correspond to the elements of the information and are associated with a respective one of the at least one character strings.

The present disclosure provides a method and a device for generating an image. The method includes: a, obtaining a character recognition result corresponding to a first image, the character recognition result including one or more characters and a first confidence of each character; b, determining a second confidence of a character set including at least one of the one or more characters according to the first confidence of each character in the character set; c, determining a refined character set corresponding to the first image based on the second confidence; and d, performing image processing on a sub image corresponding to the refined character set in the first image, to obtain a second image, an annotation text corresponding to the second image including the refined character set.

Disclosed are a method, terminal and computer readable storage medium for image classification. The method includes: determining an image feature vector of an image based on a convolutional neural network, where the image comprises textual information; determining a text feature vector based on the textual information and an embedded network; determining an image-text feature vector by joining the image feature vector with the text feature vector; and determining a category of the image based on a result of a deep neural network, where the result is determined based on the image feature vector, the text feature vector and the image-text feature vector.

A method for splitting text line images includes receiving a text line image and identifying that the text line image comprises a plurality of zones, wherein each zone includes text whose font differs from the text of adjacent zones. The method further includes selecting a splitting position between multiple zones and splitting the text line image at the splitting position into a plurality of image segments, wherein each image segment contains at least one zone of the text line image and performing optical character recognition on each image segment to recognize a text segment of the image segment. In certain implementations, the method further includes generating one or more confidence measurements and selecting a splitting position that corresponds to a large gradient in the confidence measurement.

Embodiments disclosed herein provide for systems and methods of separating characters associated with ligatures in digitized documents. The systems and methods provide for a ligature detection engine configured to identify the ligatures, and a ligature processing engine configured to identify and remove the glyphs attaching the separate characters forming the ligature.

A web detection system processes webpage information and performs automated feature extraction of webpages including machine processable information. In an embodiment, the web detection system determines a subset of webpages having a target characteristic by processing markup language. For a webpage of the subset, the web detection system determines that a first image overlaps at least a portion of a second image in the webpage. The web detection system generates an image of the webpage such that the portion of the second image is obscured by the first image. The web detection system determines a graphical feature of the webpage by processing the image, e.g., using optical character recognition. Responsive to determining that the graphical feature corresponds to graphical features of images of a different set of webpages associated with a target entity, the web detection system determines that the webpage is also associated with the target entity.

Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDP) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.