Hello, I am Seokwon Jang, the representative and chief developer of DeepNetwork, a one-person company. I run a company that provides optical character recognition (OCR) solutions based on deep learning. OCR is a technology that recognizes characters written or printed by people, characters in photographed or scanned images, and converts them into digital text that machines can read and edit. I am developing a solution that can apply this technology to various fields.
I analyzed the issues of OCR using the latest deep learning technology, the ViT model. The ViT model divides the image into fixed-size patches, converts each patch into an embedding vector, and uses it as input to the Transformer. The Transformer has advantages such as parallel processing, long-distance dependence, and self-attention mechanism. The ViT model can have higher accuracy and fewer parameters than the CNN model.
I analyzed the model structure of improving performance through fine-tuning on an OCR-specific dataset based on a pre-trained ViT model. I am preparing to build an OCR dataset suitable for my target domain, and I am also preparing to use publicly available datasets. I analyzed finding the optimal performance by adjusting the learning speed, patch size, number of layers and number of heads of the Transformer in the ViT model.
The three key detailed issues of implementing OCR with the ViT model in the papers are as follows:
Structure and learning method of the ViT model: The ViT model divides the image into fixed-size patches, converts each patch into an embedding vector, and uses it as input to the Transformer. The ViT model is based on a pre-trained Transformer model and improves performance through additional learning on a large-scale image dataset or multi-modal learning using text and images together.
Application of ViT model to OCR: To apply the ViT model to OCR, it detects the character area in the image, divides each character area into patches, and uses it as input to the ViT model. The output of the ViT model is defined as a classification problem that predicts the character label corresponding to each patch. The ViT model improves performance through fine-tuning on an OCR-specific dataset.
Advantages and limitations of the ViT model: The ViT model can apply the advantages of the Transformer, such as parallel processing, long-distance dependence, and self-attention mechanism, to image processing. The ViT model can have higher accuracy and fewer parameters than the CNN model. However, the ViT model requires more learning data and longer learning time than the CNN model, and the patch division method can lose spatial information in the image.
The preparations needed for DeepNetwork, a one-person company, to implement OCR with the ViT model are as follows:
Securing a pre-trained ViT model: It is effective to use a model pre-trained on a large-scale image dataset for the ViT model. DeepNetwork, a one-person company, needs to secure a pre-trained ViT model by downloading, purchasing, or directly learning a publicly available model.
Building a dataset for OCR: The ViT model improves performance through fine-tuning on an OCR-specific dataset. DeepNetwork, a one-person company, needs to build an OCR dataset suitable for its target domain or use a publicly available dataset. The dataset must include the character area in the image and each character label.
Optimization and evaluation of the ViT model: To apply the ViT model to OCR, you need to set appropriate hyperparameters and learning methods. DeepNetwork, a one-person company, needs to find the optimal performance by adjusting the learning speed, patch size, number of layers and number of heads of the ViT model. In addition, to quantitatively evaluate the OCR performance of the ViT model, you need to set appropriate evaluation indicators and standards.
Deep Network, a one-person startup specializing in consulting for super-large language models
E-mail : sayhi7@daum.net
Representative of a one-person startup / SeokWeon Jang