In some embodiments, apparatuses and methods are provided herein useful to train a machine learning algorithm to detect text of interest. In some embodiments, there is provided a system to detect vertically oriented text of interest including a first data set comprising a plurality of captured digital images each depicting an object of interest and a second data set comprising a plurality of augmented digital images each depicting a captured digital image augmented with a synthetic text image; a first control circuit configured to cause the machine learning algorithm to output a machine learning model trained to automatically detect occurrences of vertically oriented text of interest based on the first data set and the second data set; at least one camera; and a second control circuit configured to execute the machine learning model to automatically detect vertically oriented text of interest on the object of interest.

A system for generating textual instructions for manufacturers from hybrid textual and image data includes a manufacturing instruction generator that may generate a language processing module from a first training set including at least a training annotated file describing at least a first product to manufacture, the at least an annotated file containing one or more textual data, and at least an instruction set containing one or more manufacturing instructions to manufacture the at least a first product. Manufacturing instruction generator may use the language processing to generate textual instructions for manufacturers from at least an annotated file and may initiate manufacture using the generated manufacturing instructions.

A text classifying apparatus (100), an optical character recognition unit (1), a text classifying method (S220) and a program are provided for performing the classification of text. A segmentation unit (110) segments an image into a plurality of lines of text (401-412; 451-457; 501-504; 701-705) (S221). A selection unit (120) selects a line of text from the plurality of lines of text (S222-S223). An identification unit (130) identifies a sequence of classes corresponding to the selected line of text (S224). A recording unit (140) records, for the selected line of text, a global class corresponding to a class of the sequence of classes (S225-S226). A classification unit (150) classifies the image according to the global class, based on a confidence level of the global class (S227-S228).

Methods, apparatus, systems, and articles of manufacture are disclosed to decode receipts based on neural graph architecture. An example apparatus for decoding receipts includes, vertex feature representation circuitry to extract features from optical-character-recognition (OCR) words, polar coordinate circuitry to: calculate polar coordinates of the OCR words based on respective ones of the extracted features, graph neural network circuitry to generate an adjacency matrix based on the extracted features, post-processing circuitry to traverse the adjacency matrix to generate cliques of OCR processed words, and output circuitry to generate lines of text based on the cliques of OCR processed words.

Disclosed is a transparency detecting method based on machine vision. The transparency detecting method includes 1) operating a Secchi disk to start the water transparency measurement, and turning on the camera for shooting; 2). determining a critical position of the Secchi disk; 3) identifying a water ruler and calculating a reading of the water ruler; 4) outputting and displaying the calculated reading.

A content capturing system is suitable for capturing content in an image of a document. The content capturing system includes a processor and a storage device. The processor accesses the program stored in the storage device to implement a cutting module and a processing module. The cutting module receives a corrected image. The content in the corrected image includes a plurality of text areas, and the cutting module inputs the corrected image or a first text area into a convolutional neural network. The convolutional neural network outputs the coordinates of the first text area. The cutting module cuts the first text area according to the coordinates of the first text area. The cutting module inputs the cut first text area into a text recognition system and obtains a plurality of first characters in the first text area from the text recognition system.

An image outputting device includes a scanning module, a storage module, an identification module, a calibration module and a printing module. The scanning module generates an image by scanning an object. The storage module stores the image. The identification module identifies a layout orientation and a text orientation of the image. The calibration module calibrates the layout orientation and the text orientation of the image according to a relation of an identification result of the identification module and a layout orientation of the printing medium. When the printing module prints the calibrated image on a printing surface of the printing medium and outputs the printing medium with the printing surface facing downwardly, the layout orientation of the printed image matches with the layout orientation of the printing medium, and the text orientation of the printed image is opposite to an medium outputting direction or arranged from proximal to distal.

A content capturing system is suitable for capturing content in an image of a document. The content capturing system includes a processor and a storage device. The processor accesses the program stored in the storage device to implement a cutting module and a processing module. The cutting module receives a corrected image. The content in the corrected image includes a plurality of text areas, and the cutting module inputs the corrected image or a first text area into a convolutional neural network. The convolutional neural network outputs the coordinates of the first text area. The cutting module cuts the first text area according to the coordinates of the first text area. The cutting module inputs the cut first text area into a text recognition system and obtains a plurality of first characters in the first text area from the text recognition system.

A text recognition method and apparatus, a storage medium, and an electronic device. Said method comprises: acquiring a text image (S101); determining at least one text box from the text image, each text box corresponding to at least one word (S102); determining, from the at least one text box, a text box to be recognized (S103); determining, from the text image, a picture unit corresponding to the text box to be recognized (S104); rotating the picture unit to a target pose (S105); and performing text recognition on the picture unit in the target pose, and acquiring a target recognition result (S106). The method, apparatus, storage medium, and electronic device can recognize a text unit in a text having a large inclination angle.

Provided is an image processing apparatus that improves the performance when performing image processing accompanying an analysis. An image acquisition unit acquires processing image data for performing processing. A normal image processing unit performs normal image processing for normal output on the processing image data acquired by the image acquisition unit and outputs the normal image data. An analysis image processing unit performs analysis image processing accompanied by analysis in parallel with the normal image processing by the normal image processing unit and outputs the analysis image data. A merge processing unit merges the normal image data output by the normal image processing unit and the analysis image data output by the analysis image processing unit and outputs print data for printing.