Hangzhou Dana Technology Inc.

Hangzhou Ruisheng Software Co., Ltd.

Hangzhou Glorify Software Limited

Mingquan Chen

Huan Luo

Tao He

Lihui Mao

Fan Shi

Qingsong Xu

An object recognition processing method, a processing apparatus, an electronic device, and a non-transitory computer-readable storage medium. The method includes: obtaining an object to be recognized; recognizing a type of the object on the basis of a type recognition model; determining a processing rule corresponding to the object; in response to the fact that the type of the object is a basic type, taking the object as a target object to be recognized, and in response to the fact that the type of the object is a non-basic type, transforming the object by means of a transformer learning model, so as to transform the object into the target object to be recognized; and performing, by means of the transformer learning model, recognition processing on the target object to be recognized to obtain a target result corresponding to the target object to be recognized.

Object recognition processing method, processing apparatus, electronic device, and storage medium

A handwritten content removing method and device and a storage medium. The handwritten content removing method comprises: acquiring an input image of a text page to be processed, the input image comprising a handwritten region, which comprises a handwritten content (S); identifying the input image so as to determine the handwritten content in the handwritten region (S); and removing the handwritten content in the input image so as to obtain an output image (S).

Handwritten content removing method and device and storage medium

The disclosure provides an answer correction method and a device, including: determining the target test paper that matches the test paper to be corrected; marking the area of each answer in the test paper to be corrected as the first answer set, and marking the area of each answer in the target test paper as the second answer set; matching each answer area in the first answer set and the second answer set, and adjusting the position of the answer area in the first answer set on the test paper to be corrected; for each answer area in the second answer set, determining the target answer area in the first answer set according to the position information of the answer area on the target test paper, and correcting the answer in the determined target answer area according to the answer in the answer area. The disclosure can solve the problem in the related art that the accurate position of the answer filled by the student cannot be identified, thereby affecting the correction of the answer.

Answer correction method and device

The present disclosure provides a question correction method and device for oral calculation questions. The feature vector of the question to be searched is obtained according to the content of token in the stem of each question to be searched, and then the feature vector of each question to be searched is used to search for the target test paper that matches the test paper to be searched in the question bank. For the question to be searched in the form of oral calculation question, a second search is performed in the target test paper based on the feature vector of the question, and the search criterion is the minimum shortest editing distance. If the question type of the matched target question is also an oral calculation question, it is determined that the question to be searched is the oral calculation question to be corrected, then a preset oral calculation engine is used to calculate the oral calculation question to be corrected and the calculation result is output as the answer to the oral calculation question to be corrected. By applying the solution provided by the present disclosure, the accuracy of correction on oral calculation questions can be improved.

Question correction method, device, electronic equipment and storage medium for oral calculation questions

Display screen with animated graphical user interface

The disclosure provides a calculation practicing method, a system, an electronic device and a computer readable storage medium, the calculation practicing method includes: providing a calculation question; identifying the type and content of the calculation question; generating an answer area according to the type and content of the calculation question; receiving an answering operation in which the user inputs the answer string for the calculation question in the answer area; identifying the answer string inputted by the user; and determining whether each of the answer characters in the answer string is correct, if there is an incorrect answer character, it will be marked, so that the calculation practice can be realized through the electronic device, which is convenient for students to carry out training.

Calculation practicing method, system, electronic device and computer readable storage medium

A method and a system for intelligent identification and correction of a question, which implements, through artificial intelligence, automatic identification of stems of various types of questions and contents of answering in test papers and auto-corrects the contents of answering. The technical solution is: to train a neural network model (e.g., CNN model) into a model for question type recognition based on numerous test paper samples in advance, and to use this trained model to detect the position of individual questions, the type corresponding to the question, the different components of the question (stem, answer, picture) in the test paper, and then to identify the printed font character information of the stem and the handwriting font character information of the answer part based on the recognition model, and finally to auto-correct based on the identified character information.

Method and system for intelligent identification and correction of questions

An examination paper correction method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining a first image of a standard examination paper; identifying an area and characters of each standard answer in the first image, and using a marking box to mark an answering area where each standard answer is located; determining position information of each marking box; obtaining a second image of an examination paper to be corrected; determining, according to the position information of each marking box of the first image, an answering area in the second image matching a position of the marking box, and using a marking box to mark the determined answering area; identifying characters of an answer to be corrected in each marking box of the second image; and comparing the characters of the standard answer with the characters of the answer to be corrected.

Examination paper correction method and apparatus, electronic device, and storage medium

A method and a device for generating a collection of incorrectly-answered questions are provided. The method includes: acquiring an image of a marked test paper (S); recognizing regions of respective questions in the marked test paper according to a pre-trained first region recognition model (S); recognizing a question whose marking result is incorrect in the marked test paper as an incorrectly-answered question according to a pre-trained incorrectly-answered question recognition model (S); and storing the region of the incorrectly-answered question in an incorrectly-answered question database to generate the collection of incorrectly-answered questions (S). The above solution may solve the problem of low efficiency in generating the collection of incorrectly-answered questions in the prior art.

Method and device for generating collection of incorrectly-answered questions

A method for identifying information carried on a sheet is disclosed. The method comprises: identifying, using one or more computing devices, each of one or more areas on the sheet based on an image of the sheet and a pre-trained first model, wherein each of the one or more areas is associated with all or part of the information carried on the sheet, and the first model is a neural network based model; and identifying, using one or more computing devices, characters in each of the one or more areas based on the image of the sheet, each of the one or more areas and a pre-trained second model so as to determine the information carried on the sheet, wherein the second model is a neural network based model.

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Zhejiang, China

Mingquan Chen