DTLR

Handwritten text recognition and character detection model.

CommonProductProductivityOCRHandwritten Recognition

DTLR is a detection-based handwritten text line recognition model, improved from DINO-DETR, designed for text recognition and character detection. The model is pre-trained on synthetic data and then fine-tuned on real datasets. It holds significant relevance in the OCR (Optical Character Recognition) field, especially in enhancing the accuracy and efficiency of handwritten text processing.

An improved model based on DINO-DETR for text recognition and character detection.
Pre-trained on synthetic data to enhance the model's generalization capabilities.
Fine-tuned on real datasets using CTC loss to optimize model performance.
Supports multiple languages and character sets
including Latin
French
German
and Chinese.
Provides weight files for pre-trained and fine-tuned models.
Includes an N-gram model for assessing and improving recognition accuracy.

\This product is suitable for researchers and developers in the field of OCR
particularly those who specialize in handwritten text recognition tasks. It can help improve recognition accuracy and efficiency
saving substantial manual proofreading time.\

Used for recognizing and transcribing handwritten texts in historical documents.
In the medical field
utilized to decipher handwritten prescriptions by doctors.
In education
applied for the automatic grading of students' handwritten assignments.

1. Clone the code repository to your local environment.
2. Create a virtual environment and install the necessary Python dependencies.
3. Install the version of PyTorch compatible with your system and CUDA version according to the guidelines.
4. Place the dataset in the designated folder and perform necessary preprocessing.
5. Download the pre-trained model weights and place them in the appropriate directory.

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