Authors: Raphael Baena, Syrine Kalleli , Mathieu Aubry

Affiliation: ENPC Imagine

Description: We employ a transformer-based architecture that detects characters in parallel, ensuring fast and accurate predictions. For each character, it provides a boundary box and its likelihood, which are then used for Optical Character Recognition (OCR). Notably, this approach doesn't rely on any language prior.

We first pre-trained the architecture on synthetic data consisting of text lines with characters from various fonts. We use standard classification and bounding box positioning losses for this process.

Then, we can finetune the architecture on real datasets. Unlike synthetic data, these datasets do not include ground truth bounding boxes, but only text transcriptions. Therefore, we can't use the same training losses as before. Instead, we use the pre-trained model to detect the characters' bounding boxes. We then organize the characters based on these bounding boxes and compute the Connectionist Temporal Classification (CTC) loss. During fine-tuning, our approach demonstrates the ability to learn the bounding boxes of new characters.

Authors: Jiaqianwen

Description: With cnn_encoder_chars_998_post as the pretrained model, change the dictionary, and finetune the model parameter.

Authors: The HR-Ciphers 2024 organizers

Affiliation: Computer Vision Center

Description: An Long Short-Term Memory (LSTM) Recurrent Neural Network model inspired by Baró et al. "Optical Music Recognition by Long Short-Term Memory Networks", GREC 2017