During an image-analysis technique, the system calculates features by performing image analysis (such as optical character recognition) on a received image of a document. Using these features, as well as spatial and grammatical constraints, the system determines a layout of the document. For example, the layout may be determined using constraint-based optimization based on the spatial and the grammatical constraints. Note that the layout specifies locations of content in the document, and may be used to subsequently extract the content from the image and/or to allow a user to provide feedback on the extracted content by presenting the extracted content to the user in a context (i.e., the determined layout) that is familiar to the user.

During an image-analysis technique, the system calculates features by performing image analysis (such as optical character recognition) on a received image of a document. Using these features, as well as spatial and grammatical constraints, the system determines a layout of the document. For example, the layout may be determined using constraint-based optimization based on the spatial and the grammatical constraints. Note that the layout specifies locations of content in the document, and may be used to subsequently extract the content from the image and/or to allow a user to provide feedback on the extracted content by presenting the extracted content to the user in a context (i.e., the determined layout) that is familiar to the user.

The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed carry out an initial processing step on one or more scanned images to identify a subset of the total number of symbols frequently used in the scanned document image or images. One or more lists of graphemes for the language of the text are then ordered in most-likely-occurring to least-likely-occurring order to facilitate a second optical-character-recognition step in which symbol images extracted from the one or more scanned-document images are associated with one or more graphemes most likely to correspond to the scanned symbol image.

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.

A method for reading an optical prescription on an optical prescription image. The method includes detecting a region comprising the optical prescription on the optical prescription image; extracting the optical prescription and converting the optical prescription into machine-encoded optical prescription data; classifying a portion of the optical prescription data into one or more predetermined categories, to generate an optical prescription value associated with a respective one of the one or more predetermined categories; and determining whether the optical prescription value associated with the respective one of the one or more predetermined categories contains an error, and, if the optical prescription value contains the error, correcting the error within the optical prescription value, to generate a corrected optical prescription value associated with the respective one of the one or more predetermined categories. A system for reading an optical prescription on an optical prescription is also disclosed.

Some implementations of the disclosure describe a non-transitory computer-readable medium having executable instructions stored thereon that, when executed by a processor, cause the processor to perform operations comprising: obtaining a document image file including a first image corresponding to a first page of a document; generating, using a first trained model, based on the first image, a first prediction that the first page includes a term identifying a class of people, the first prediction including a first location of the term within the first image; in response to generating the first prediction that the first page includes the term identifying the class of people, generating, using a second trained model, based on the first image, a second prediction of whether or not the first page includes a section that uses a term identifying a class of people in a specific context.