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Aspose.OCR for Python via .NET 25.8.0 - Release Notes
What was changed
| Key | Summary | Category |
|---|---|---|
| OCRNET‑1022 | LLM integration. | New feature |
| OCRNET‑1071 | Integrate LLM into table recognition. | New feature |
| OCRNET‑1079 | Improve excel output with LLMs table recognition. | New feature |
| OCRNET‑1067 | Fix bug with multilaguages with multithreading. | Bug fix |
Public API changes and backwards compatibility
This section lists all public API changes introduced in Aspose.OCR for Python via .NET 25.8.0 that August affect the code of existing applications.
Added public APIs:
AI Component
⚠️ Disclaimer:
By using AI-powered features, you are solely responsible for ensuring compliance with any
applicable laws, licensing terms, third-party AI model usage policies, and data privacy regulations.
Aspose does not take responsibility for the accuracy, licensing, or reliability of external AI models.
⚠️ Disclaimer:
Available only for Windows x64
The AsposeAI class provides the bridge between LLM models and the OCR engine.
The AsposeAI class now provides more flexibility in integrating AI models for spell-checking, intelligent postprocessing, and structured table extraction.
🛠 Constructors
AsposeAI()
public AsposeAI(logger) #ILogger
You can also pass optional logging and customization callbacks.
Configuration
⚙️ AsposeAIModelConfig
| Property | Type | Description |
|---|---|---|
allow_auto_download | string | If true, the model will be automatically downloaded if not available locally. |
directory_model_path | string | Optional path where downloaded or processed models will be cached. If not set, a default system location will be used. |
file_model_path | string | Local path to the folder containing the model files. If specified, this will be used instead of downloading. Default empty. |
hugging_face_quantization | string | Optional quantization type to use when downloading from HuggingFace. Examples: “int8”, “fp16”, “none”. Default q4_k_m. |
hugging_face_repo_id | string | ID of the model on HuggingFace (e.g., “openai/gpt2”). If specified, the model will be downloaded from HuggingFace. Default bartowski/Qwen2.5-3B-Instruct-GGUF. |
context_size | int | Maximum number of tokens considered during inference. Default uses model’s default context. |
gpu_layers | int | Number of GPU layers for model inference. Set 0 to run on CPU. Default: 40. |
🧠 AsposeAI Class – Core Methods
| Method | Type | Description |
| ———————————————— | | ——————————————————————- |
| set_post_processor(processor, custom_settings) | aspose.ocr.ai.IOcrAIPostProcessor, aspose.ocr.AsposeAIModelConfig| Adds a custom AI postprocessor to enhance OCR results. |
| run_postprocessor(texts) | Array of string | Enhances plain recognized text strings using registered AI modules. |
| run_postprocessor(output) | aspose.ocr.OcrOutput | Enhances structured OCR output using registered AI modules. |
| free_resources() | | Explicitly unloads AI models and clears memory. |
| list_local() | | Lists all local AI models available in the configured folder. |
| get_local_path() | | Returns the directory path of the current model location. |
| is_initialized() | | Checks if the AI engine and model are ready to use. |
🔤 SpellCheckAIProcessor Class – Postprocessor Module
A built-in module that performs AI-powered spell-check correction.
| Method | Description |
|---|---|
get_result() | Returns extracted structured tables as AIResult objects. |
save_txt(path) | Saves processed results into .txt format. |
- Usage: Register using
AsposeAI.set_post_processor(SpellCheckAIProcessor(),AsposeAIModelConfig())
📄 TableAIProcessor Class — Postprocessor Module
Implements: IOcrAIPostProcessor
Performs AI-driven table detection, extraction, and reconstruction.
| Method | Description |
|---|---|
get_result() | Returns extracted structured tables as AIResult objects. |
save_txt(path) | Saves processed results into .txt format. |
save_xlsx(path) | Saves structured tables into an .xlsx Excel file. |
- Usage: Register using
AsposeAI.set_post_processor(TableAIProcessor(),AsposeAIModelConfig())
AITableDetectionMode — a new enum for controlling AI-powered table detection modes:
NONE— Does not explicitly detect tables, processes the entire image as if it were a single table.
Returns both recognized text and detected tables but does not crop them from the image.AUTO— Automatically detects tables, extracts them from surrounding text,
applies proper table layout, and returns fully structured, well-marked tables.
AIResult — a new class representing the transformed and processed result of text recognition.
| Property | Type | Description |
|---|---|---|
file_name | string | Gets or sets the name of the image whose recognition result was additionally processed by the LLM model. |
recognition_text | string | Gets or sets the transformed and processed result of text recognition. |
🔗 API References
AsposeAI
Core class to load, configure, and apply AI models (e.g., for spell correction) to OCR results.SpellCheckAIProcessor
Built-in AI postprocessor that uses a language model to fix spelling mistakes in recognized text.TableAIProcessor
Built-in AI postprocessor that uses a language model to fix spelling mistakes in recognized text.
Updated public APIs:
No changes.
Deprecated APIs
The following public APIs have been marked as deprecated and will be removed in 25.10.0 (October 2025) release:
RectangleOutput class
AsposeOcr.detect_rectangles method
RecognitionResult.recognition_areas_text
RecognitionResult.recognition_areas_rectangles
RecognitionResult.skew
CharacterRecognitionResult.image_index
SkewOutput.image_index
RecognitionResult.skew
RecognitionResult.skew
RecognitionResult.skew
Removed public APIs:
No changes.
Examples
The code samples below illustrate the changes introduced in this release:
Enable Spell-check AI postprocessor
from aspose.ocr import *
from aspose.ocr.ai import *
from aspose.ocr.models import *
# Initialize recognition API
api = AsposeOcr()
# Add an image to OcrInput object
input = OcrInput(InputType.SINGLE_IMAGE)
input.add("source.png")
# Recognize image
results = api.recognize(input)
# Initialize AI API
ai = AsposeAI()
config = AsposeAIModelConfig()
proces = SpellCheckAIProcessor()
ai.set_post_processor(proces, config)
ai.run_postprocessor(result, AITableDetectionMode.AUTO)
corrected = proces.get_result()
print(corrected[0].recognition_text)
ai.free_resources()
Enable Table AI postprocessor
from aspose.ocr import *
from aspose.ocr.ai import *
from aspose.ocr.models import *
# Initialize recognition API
api = AsposeOcr()
# Add an image to OcrInput object
input = OcrInput(InputType.SINGLE_IMAGE)
input.add("source.png")
# Recognize image
results = api.recognize(input)
# Initialize AI API
ai = AsposeAI()
config = AsposeAIModelConfig()
proces = TableAIProcessor()
ai.set_post_processor(proces, config)
ai.run_postprocessor(result, AITableDetectionMode.AUTO)
corrected = proces.get_result()
print(corrected[0].recognition_text)
ai.free_resources()