Abstract: Disclosed are a camera module and a method thereof, and a terminal which relates to the field of camera technology. The camera comprises a circuit board, a sensitive chip, a bracket, and a sealing member. At least one side of the bracket is an open side, and a receiving cavity is defined between a surface of the open side facing the circuit board and a first surface of the circuit board. The sealing member is filled in the receiving cavity. The camera module improves the space utilization efficiency, reduces the width of the open side, increases the screen-to-body ratio of the terminal, and improves user's perception and user experience.

Abstract: The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Abstract: A method of processing a task, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence, in particular to fields of deep learning and computer vision, and may be applied to OCR optical character recognition and other scenarios. The method includes: parsing labeled data to be processed according to a task type identification, to obtain task labeled data, a tag information of the task labeled data is matched with the task type identification, and the task labeled data includes first task labeled data and second task labeled data; training a model using the first task labeled data, to obtain candidate models, the model is determined according to the task type identification; and determining a target model from the candidate models according to a performance evaluation result obtained by performing performance evaluation on the plurality of candidate models using the second task labeled data.

Abstract: A method and a platform of generating a document, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a text recognition scenario and other scenarios. The method includes: performing a category recognition on a document picture to obtain a target category result; determining a target structured model matched with the target category result; and performing, by using the target structured model, a structure recognition on the document picture to obtain a structure recognition result, so as to generate an electronic document based on the structure recognition result, wherein the structure recognition result includes a field attribute recognition result and a field position recognition result.

Abstract: A method for training a model includes: obtaining a scene image, second actual characters in the scene image and a second construct image; obtaining first features and first recognition characters of characters obtained by performing character recognition on the scene image using the model to be trained; obtaining second features of characters obtained by performing character recognition on the second construct image using the training auxiliary model; and obtaining a character recognition model by adjusting model parameters of the model to be trained based on the first recognition characters, the second actual characters, the first features and the second features.

Abstract: The present disclosure provides a training method of a text recognition model, a text recognition method, and an apparatus, relating to the technical field of artificial intelligence, and specifically, to the technical field of deep learning and computer vision, which can be applied in scenarios such as optional character recognition, etc. The specific implementation solution is: performing mask prediction on visual features of an acquired sample image, to obtain a predicted visual feature; performing mask prediction on semantic features of acquired sample text, to obtain a predicted semantic feature, where the sample image includes text; determining a first loss value of the text of the sample image according to the predicted visual feature; determining a second loss value of the sample text according to the predicted semantic feature; training, according to the first loss value and the second loss value, to obtain the text recognition model.

Abstract: The disclosure discloses a method for character recognition, an electronic device, and a storage medium. The technical solution includes: obtaining a test sample image and a test sample character both corresponding to a test task; performing fine-tuning on a trained meta-learning model based on the test sample image and the test sample character to obtain a test task model; obtaining a test image corresponding to the test task; and generating a test character corresponding to the test image by inputting the test image into the test task model.

Abstract: A camera module includes a support base, an optical filter and a lens assembly. The lens assembly is installed on the support base. The support base includes a plastic platform and a metal frame. The metal frame is bonded to the plastic platform. The optical filter is sandwiched between the plastic platform and the metal frame. The present invention further involves a mobile terminal. The support base is formed by bonding the plastic platform to the metal frame and bonding the optical filter between the plastic platform and the metal frame. The metal frame embedded in the plastic platform can greatly increase the strength of the support base, and besides, the optical filter is fixedly sandwiched between the plastic platform and the metal frame without needing to further fix the optical filter by using adhesives, preventing the optical filter from becoming dirty in the process of fixing.

Abstract: Disclosed are a camera module and a method thereof, and a terminal which relates to the field of camera technology. The camera comprises a circuit board, a sensitive chip, a bracket, and a sealing member. At least one side of the bracket is an open side, and a receiving cavity is defined between a surface of the open side facing the circuit board and a first surface of the circuit board. The sealing member is filled in the receiving cavity. The camera module improves the space utilization efficiency, reduces the width of the open side, increases the screen-to-body ratio of the terminal, and improves user's perception and user experience.

Abstract: A camera module includes a support base, an optical filter and a lens assembly. The lens assembly is installed on the support base. The support base includes a plastic platform and a metal frame. The metal frame is bonded to the plastic platform. The optical filter is sandwiched between the plastic platform and the metal frame. The present invention further involves a mobile terminal. The support base is formed by bonding the plastic platform to the metal frame and bonding the optical filter between the plastic platform and the metal frame. The metal frame embedded in the plastic platform can greatly increase the strength of the support base, and besides, the optical filter is fixedly sandwiched between the plastic platform and the metal frame without needing to further fix the optical filter by using adhesives, preventing the optical filter from becoming dirty in the process of fixing.