A computer-implemented method, according to one embodiment, includes: receiving an image having characters that correspond to a language, and using a text recognition algorithm to determine a first language believed to correspond to the characters. A first confidence level associated with the first language is also computed, and a determination is made as to whether the first confidence level associated with the first language is outside a predetermined range. In response to determining that the first confidence level associated with the first language is not outside the predetermined range, the first language is output as the given language. The text recognition algorithm is trained using a simple shallow neural network and a generated mixed language corpus. The generated mixed language corpus is formed by: randomly sampling libraries having vocabulary and/or characters therein, and combining the randomly sampled vocabulary and/or characters to form the generated mixed language corpus.

The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

A method for text recognition is disclosed. The method includes obtaining a whole-image scenario for an image to be processed and a text image in the image to be processed. The method further includes determining a first text recognition model corresponding to the whole-image scenario. The method further includes performing text recognition on the text image according to the first text recognition model to obtain text information.

A method for text recognition is disclosed. The method includes obtaining a whole-image scenario for an image to be processed and a text image in the image to be processed. The method further includes determining a first text recognition model corresponding to the whole-image scenario. The method further includes performing text recognition on the text image according to the first text recognition model to obtain text information.

There are provided an image analysis apparatus, an image analysis method, and a program for implementing an image analysis method that can, when text information about a structural formula of a compound is generated from an image showing the structural formula, cope with a change in the way of drawing of the structural formula.

An image analysis apparatus according to one embodiment of the present invention includes a processor, and the processor is configured to generate, on the basis of a feature value of a subject image showing a structural formula of a subject compound, symbol information representing the structural formula of the subject compound with a line notation, by using an analysis model. The analysis model is a model created through machine learning using a learning image and symbol information representing a structural formula of a compound shown by the learning image with a line notation.

There are provided an image analysis apparatus, an image analysis method, and a program for implementing an image analysis method that can, when text information about a structural formula of a compound is generated from an image showing the structural formula, cope with a change in the way of drawing of the structural formula.

An image analysis apparatus according to one embodiment of the present invention includes a processor, and the processor is configured to generate, on the basis of a feature value of a subject image showing a structural formula of a subject compound, symbol information representing the structural formula of the subject compound with a line notation, by using an analysis model. The analysis model is a model created through machine learning using a learning image and symbol information representing a structural formula of a compound shown by the learning image with a line notation.

A formula recognition method and apparatus, a computer-readable medium, and an electronic device. The formula recognition method includes acquiring a target image including a formula, processing the target image to obtain a global image feature and a local image feature, and processing the global image feature and the local image feature to obtain the formula included in the target image.

Systems, apparatuses, and methods are described for detecting anomalies in closed captioning or other video presentation systems. Anomaly detection may involve comparing detected captions that are delivered to one or more end devices (return captions) with corresponding scheduled captions. Other types of information may also be similarly compared between original scheduled instances of information to be delivered with the actual (return) delivered information. Such other types of information may include, for example, ratings information (such as V-chip ratings and/or flags) and/or content (e.g., advertisement) insertion information such as SCTE-35 signaling.

Systems, apparatuses, and methods are described for detecting anomalies in closed captioning or other video presentation systems. Anomaly detection may involve comparing detected captions that are delivered to one or more end devices (return captions) with corresponding scheduled captions. Other types of information may also be similarly compared between original scheduled instances of information to be delivered with the actual (return) delivered information. Such other types of information may include, for example, ratings information (such as V-chip ratings and/or flags) and/or content (e.g., advertisement) insertion information such as SCTE-35 signaling.