An image processing system performs tilt correction with respect to a document image having handwritten characters and typed letters mixed with each other. The image processing system separates the document image into an image with handwritten characters determined as handwritten characters and an image without handwritten characters not determined as handwritten characters, estimates a tilt angle of the image without handwritten characters, and corrects the document image on the basis of the tilt angle.

An image processing system performs tilt correction with respect to a document image having handwritten characters and typed letters mixed with each other. The image processing system separates the document image into an image with handwritten characters determined as handwritten characters and an image without handwritten characters not determined as handwritten characters, estimates a tilt angle of the image without handwritten characters, and corrects the document image on the basis of the tilt angle.

The character and symbol recognition system comprises a detachable body having a photographic camera to capture real time image of one of sheet or poster comprising of printed and handwritten characters and symbols; an input unit to acquire the real time captured image; a pre-processing unit to detect a character and symbol region; a classification unit equipped with at least two channel neural network based on CNN and LSTM to separate the character and symbol region; a central processing unit to calculate weights for transitions to the candidates thereby generate one of a first character or first symbol string transition data based on a set of the candidates and the weights; and a control unit to detect one or both of the printed and handwritten characters and symbols thereby display the detected information on a display unit and play the detected information on a speaker to alert a rider.

A computer-readable, non-transitory recording medium contains therein an image processing program. The image processing program is for generating learning data of a character detection model that at least detects, to recognize a character in a document contained in an image, a position of the character in the image, and configured to cause a computer to generate a cropped image by cropping the image, and adopt the cropped image not containing an image representing a split character as the learning data, instead of adopting the cropped image containing the image representing the split character as the learning data.

A computer-readable, non-transitory recording medium contains therein an image processing program. The image processing program is for generating learning data of a character detection model that at least detects, to recognize a character in a document contained in an image, a position of the character in the image, and configured to cause a computer to generate a cropped image by cropping the image, and adopt the cropped image not containing an image representing a split character as the learning data, instead of adopting the cropped image containing the image representing the split character as the learning data.

Disclosed are systems and methods for generating search result data based on machine-encoded text generated by computer vision optical character recognition machine learning techniques performed on digital media. The disclosed systems and methods provide a novel framework for performing machine learning visual search or machine learning text extraction techniques on digital media in order to extract and analyze the data therein and further conduct search queries based on the extracted and analyzed data. The disclosed framework may leverage the aforementioned computer vision machine learning techniques in order to provide a user with relevant search results regarding objects and text detect in digital media captured on a user device.

A method of price text detection by an imaging controller comprises obtaining, by the imaging controller, an image of a shelf supporting labels bearing price text, generating, by the imaging controller, a plurality of text regions containing candidate text elements from the image, assigning, by the imaging controller, a classification to each of the text regions, selected from a price text classification and a non-price text classification. The imaging controller, within each of a subset of the text regions having the price text classification: detects a price text sub-region and generates a price text string by applying character recognition to the price text sub-region. The method further includes presenting, by the imaging controller, the locations of the subset of text regions, in association with the corresponding price text strings.

Disclosed are a system, apparatus and techniques for evaluating a dataset to confirm that the data in the dataset satisfies a data quality metric. A machine learning engine or the like may evaluate text strings within the dataset may be of arbitrary length and encoded according to an encoding standard. Data vectors of a preset length may be generated from the evaluated text strings using various techniques. Each data vector may be representative of the content of the text string and a category may be assigned to the respective data vector. The category assigned to each data vectors may be evaluated with respect to other data vectors in the dataset to determine compliance with a quality metric. In the case that a number of data vectors fail to meet a predetermined quality metric, an alert may be generated to mitigate any system errors that may result from unsatisfactory data quality.

Provided are a method, a system, and a non-transitory computer-readable record medium for comparing common features of products and providing a comparison result. A product comparison method includes recognizing at least two comparable products from at least one image; displaying at least one common attribute of the at least two comparable products through a user interface; and based on the user interface receiving a user input that selects one of the at least one common attribute, as a selected attribute, providing a result of comparison between the at least two comparable products with regard to the selected attribute.

In some implementations, a device may receive an image that depicts handwritten text. The device may determine that a section of the image includes the handwritten text. The device may analyze, using a first image processing technique, the section to identify subsections of the section that include individual words of the handwritten text. The device may reconfigure, using a second image processing technique, the subsections to create preprocessed word images associated with the individual words. The device may analyze, using a word recognition model, the preprocessed word images to generate digitized words that are associated with the preprocessed word images. The device may verify, based on a reference data structure, that the digitized words correspond to recognized words of the word recognition model. The device may generate, based on verifying the digitized words, digital text according to a sequence of the digitized words in the section.