Provided are method and apparatus for data structuring of text. The apparatus for data structuring of text includes a data extraction unit configured to extract text and location information of the text from an image based on an optical character recognition (OCR) technique, a data processing unit configured to generate a text unit based on the text and the location information, a form classification unit configured to classify a form of the image based on the text, a labeling unit configured to label the text unit as first text, second text, and third text respectively corresponding to an item name, an item value, or others based on the classified form, a relationship identification unit configured to map and structure the second text corresponding to the first text, and a misrecognition correction unit configured to determine misrecognition of the first text and correct the first text determined to be misrecognized.

Provided are method and apparatus for data structuring of text. The apparatus for data structuring of text includes a data extraction unit configured to extract text and location information of the text from an image based on an optical character recognition (OCR) technique, a data processing unit configured to generate a text unit based on the text and the location information, a form classification unit configured to classify a form of the image based on the text, a labeling unit configured to label the text unit as first text, second text, and third text respectively corresponding to an item name, an item value, or others based on the classified form, a relationship identification unit configured to map and structure the second text corresponding to the first text, and a misrecognition correction unit configured to determine misrecognition of the first text and correct the first text determined to be misrecognized.

A display control integrated circuit (IC) applicable to performing real-time video content text detection and speech automatic generation in a display device may include a pre-processing circuit, a character recognition circuit and a post-processing circuit. The pre-processing circuit may input a video signal to obtain a real-time video content carried by the video signal, and perform preliminary text detection on the real-time video content to generate a series of segmented character images to indicate a subtitle. The character recognition circuit may perform character recognition on the series of segmented character images to generate a series of characters, respectively. The post-processing circuit may perform vocabulary correction on the series of characters to selectively replace any erroneous character with a correct character to generate one or more vocabularies, for performing speech automatic generation.

The method, system, and non-transitory computer-readable medium embodiments described herein provide for identifying invalid identification documents. In various embodiments, an application executing on a user device prompts the user device to transmit an image of the identification document. The application receives an image including the identification document in response to the identification document being within a field of view of a camera of the user device. The identification document includes a plurality of visual elements, and one or more visual elements of the plurality of visual elements are one or more invalidating marks. The application detects a predetermined pattern on the identification document in the image, the predetermined pattern formed from the one or more invalidating marks. The application determines that the identification document is invalid based on the detected predetermined pattern.

A license plate number recognition method includes: extracting license plate number features of an image to be recognized including a license plate number, through a pre-trained convolutional neural network; extracting an intermediate convolution result during extracting the license plate number features, and extracting a first verification feature and/or a second verification feature according to the intermediate convolution result; verifying whether the license plate number features are correct according to the first and/or second verification features; if correct, outputting a predicted license plate number result according to the license plate number features. During the feature extraction process of the license plate number features, an intermediate feature is extracted as a verification feature to verify whether the extracted license plate number features are correct, and only when the verification is passed, outputting the license plate number result, which reduces the output error rate of the license plate number recognition result.

A license plate number recognition method includes: extracting license plate number features of an image to be recognized including a license plate number, through a pre-trained convolutional neural network; extracting an intermediate convolution result during extracting the license plate number features, and extracting a first verification feature and/or a second verification feature according to the intermediate convolution result; verifying whether the license plate number features are correct according to the first and/or second verification features; if correct, outputting a predicted license plate number result according to the license plate number features. During the feature extraction process of the license plate number features, an intermediate feature is extracted as a verification feature to verify whether the extracted license plate number features are correct, and only when the verification is passed, outputting the license plate number result, which reduces the output error rate of the license plate number recognition result.

A license plate number recognition method includes: extracting license plate number features of an image to be recognized including a license plate number, through a pre-trained convolutional neural network; extracting an intermediate convolution result during extracting the license plate number features, and extracting a first verification feature and/or a second verification feature according to the intermediate convolution result; verifying whether the license plate number features are correct according to the first and/or second verification features; if correct, outputting a predicted license plate number result according to the license plate number features. During the feature extraction process of the license plate number features, an intermediate feature is extracted as a verification feature to verify whether the extracted license plate number features are correct, and only when the verification is passed, outputting the license plate number result, which reduces the output error rate of the license plate number recognition result.

A license plate number recognition method includes: extracting license plate number features of an image to be recognized including a license plate number, through a pre-trained convolutional neural network; extracting an intermediate convolution result during extracting the license plate number features, and extracting a first verification feature and/or a second verification feature according to the intermediate convolution result; verifying whether the license plate number features are correct according to the first and/or second verification features; if correct, outputting a predicted license plate number result according to the license plate number features. During the feature extraction process of the license plate number features, an intermediate feature is extracted as a verification feature to verify whether the extracted license plate number features are correct, and only when the verification is passed, outputting the license plate number result, which reduces the output error rate of the license plate number recognition result.

An image processing apparatus comprises: an acquiring unit configured to acquire image data generated by reading an original; a specifying unit configured to specify an object region including a predetermined object in the image data; a removing unit configured to remove, from the object region, noise with a size smaller than a size specified by a first threshold; and a setting unit configured to set the first threshold, for each object region specified by the specifying unit, in accordance with the size of the object region.

Techniques are disclosed relating to methods that include receiving, by a computer system, a plurality of images of an object taken from a video during which there is relative movement between the object and a camera that captures the video. The method may further include in response to determining that the video does not include a single image that meets a clarity threshold for the object, creating, by the computer system, a merged image of the object by combining portions of different images of the plurality of images such that the clarity threshold for the object is satisfied by the merged image. The method may also include capturing, by the computer system, information about the object using the merged image.