A handwriting numbered musical notation recognition system includes: a coding rules database; a music file database for storing a large numbers of music expression files which are collected and edited by computer used formats; a conversion unit for converting music expression files into numbered musical notation files; a handwriting database for storing relations between music symbols and handwriting trace data for numbered musical notations, and a handwriting combination unit connected to the handwriting trace database and the numbered musical notation coding sequences database; and a handwriting numbered musical notation recognition model used for recognition of the handwriting sequence. The music creation can be easily retained by electronic forms, while the creators still can create his (or her) music creations by handwriting. Furthermore, the recognition of the present invention supports the use of MusicXML formats, MIDI formats, or other computer formats.

A handwriting numbered musical notation recognition system includes: a coding rules database; a music file database for storing a large numbers of music expression files which are collected and edited by computer used formats; a conversion unit for converting music expression files into numbered musical notation files; a handwriting database for storing relations between music symbols and handwriting trace data for numbered musical notations, and a handwriting combination unit connected to the handwriting trace database and the numbered musical notation coding sequences database; and a handwriting numbered musical notation recognition model used for recognition of the handwriting sequence. The music creation can be easily retained by electronic forms, while the creators still can create his (or her) music creations by handwriting. Furthermore, the recognition of the present invention supports the use of MusicXML formats, MIDI formats, or other computer formats.

A musical score image analyzer includes a processor and a memory having stored thereon instructions executable by the processor to cause the musical score image analyzer to perform: detecting musical symbols in a musical score image obtained by capturing a musical score having a plurality of staffs arranged in parallel to each other and the musical symbols respectively disposed in prescribed positions in the staffs; specifying a symbol column having the detected musical symbols which are arranged in a column; calculating an index relating an image capturing based on the symbol column; and instructing a capturing device to perform capturing operation of a still image for the musical score image when the index satisfies a prescribed condition.

An image correction device includes a line segment detection module, a shape specification module and an image correction module. The line segment detection module detects from a captured image obtained by photographing a document a plurality of line segments that correspond to the notation on the surface of the document. The shape specification module specifies shape approximation lines that approximate the surface shape of the document from the plurality of line segments. The image correction module utilizes the shape approximation lines specified by the shape specification module to correct the captured image.

A musical score editing device includes a processor that implements instructions to execute tasks. The tasks include: an image recognition task that recognizes an image of a musical score as first image data; a pitch reception task that receives designation of a pitch amount indicating a difference between keys before and after transposition; a musical note recognition task that recognizes one or more musical notes in the first image data; a musical note deletion task that outputs second image data obtained by deleting the musical notes from the first image data; a musical note transposition task that outputs third image data indicating an image of one or more musical notes after the transposition based on the pitch amount; and a synthesis task that synthesizes the second image data and the third image data to output fourth image data indicating an image of a musical score after the transposition.

A musical score editing device includes a processor that implements instructions to execute tasks. The tasks include: an image recognition task that recognizes an image of a musical score as first image data; a pitch reception task that receives designation of a pitch amount indicating a difference between keys before and after transposition; a musical note recognition task that recognizes one or more musical notes in the first image data; a musical note deletion task that outputs second image data obtained by deleting the musical notes from the first image data; a musical note transposition task that outputs third image data indicating an image of one or more musical notes after the transposition based on the pitch amount; and a synthesis task that synthesizes the second image data and the third image data to output fourth image data indicating an image of a musical score after the transposition.

Disclosed are music symbol recognition apparatuses and methods that recognise music symbols from handwritten music notations. Various implementations may process handwritten music notations by segmenting the handwritten music notations into a plurality of elementary ink segments and then grouping the segments into graphical objects based on spatial relationships between the segments. One or more candidate music symbols may be determined for each graphical object, along with a symbol cost for each symbol, which represents a likelihood that the graphical object belongs to a predetermined class of symbols. The music symbol candidates may be parsed to form graphs based on grammar rules, and the graph most likely to represent the handwritten music notations may be selected for display or other use. The selection may be based on the symbol costs associated with each candidate and on spatial costs associated with the grammar rules that are applied to the candidates.

A method of rectifying a text image, a training method, an electronic device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, deep learning technology, intelligent transportation and high-precision maps. An exemplary implementation includes: performing, based on a gating strategy, a plurality of first layer-wise processing on a text image to be rectified, so as to obtain respective feature maps of a plurality of layer levels, wherein each of the feature maps includes a text structural feature related to the text image to be rectified, and the gating strategy is configured to increase an attention to the text structural feature; and performing a plurality of second layer-wise processing on the respective feature maps of the plurality of layer levels, so as to obtain a rectified text image corresponding to the text image to be rectified.