New era of OCR: the evolution to intelligent and versatile systems | Best llm models | Large language models courses | Generative ai examples | Turtles AI

OCR-1.0 is becoming obsolete due to the complexity and variety of modern optical characters. The new GOT model, part of OCR-2.0, is a unified system that handles complex, multipage input with formatted output and advanced interactive features, significantly improving the effectiveness of optical processing.

 Key Points:

•  Evolution of OCR toward a unified, end-to-end model called GOT (General OCR Theory).
•  Traditional OCR limited by complexity and lack of flexibility for different types of optical characters.
•  New technologies for interactive and multipage OCR, with output generation formatted in various styles.
•  OCR models enhanced by high-compression encoders and decoders for long contexts.

With the growing need for intelligent optical character processing, conventional Optical Character Recognition (OCR), known as OCR-1.0, has shown obvious limitations. Conventional systems, which often use pipelines composed of specialized modules, suffer from systematic errors and high maintenance costs, as well as a lack of flexibility to adapt to different types of optical input, such as complex text, mathematical formulas, tables or graphs. Indeed, the traditional approach requires a fragmented process, with separate steps for layout detection, region extraction, and content recognition.

To address these challenges, a new phase is emerging: OCR-2.0. The GOT (General OCR Theory) model is the centerpiece of this evolution, proposing a unified, end-to-end system capable of handling a wide range of optical “characters,” ranging from plain text to sheet music and geometric shapes. With an architecture consisting of a highly compressive encoder and a decoder designed to handle long contexts, GOT promises superior performance and greater versatility than traditional models.

One of GOT’s key innovations is its ability to support optical inputs in various formats, such as document images and scenes, both at the full-page level and in fragments. In addition, the system allows output formatted in different styles, such as Markdown or TikZ, to be generated through simple commands. This approach not only enhances flexibility, but also introduces an interactive component: the user can guide optical recognition to specific areas of a document using coordinates or colors.

A historical challenge for OCR models has been the handling of high-resolution or multipage images, which involved complexities in processing and compressing information. GOT addresses this problem by integrating dynamic resolution technologies, which allow more efficient content handling without sacrificing recognition quality.

Experiments conducted on this new model have shown promising results, confirming its ability to overcome the limitations of traditional models. The move to OCR-2.0 thus appears to mark a major breakthrough in the management of complex optical content, reducing the need to use specialized models for each individual document type or character.

The evolution toward unified OCR models such as GOT opens up new possibilities in processing optical content, providing greater flexibility and accuracy in applications.