Fix spelling errors: infomation -> information, positons -> positions (#17911)

* fix: correct "infomation" to "information" in README.md

Fixes a spelling error in the documentation where "infomation"
was incorrectly used instead of "information".

* fix: correct "positons" to "positions" in infer_rec.py

Fixes spelling errors where "positons" was incorrectly used
instead of "positions" in multiple locations.

README.md

@@ -75,7 +75,7 @@ English | [简体中文](./readme/README_cn.md) | [繁體中文](./readme/README
 
 - Released PaddleOCR-VL:
     - **Model Introduction**:
-        - **PaddleOCR-VL** is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. **This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption**. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. The model has been released on [HuggingFace](https://huggingface.co/PaddlePaddle/PaddleOCR-VL). Everyone is welcome to download and use it! More introduction infomation can be found in [PaddleOCR-VL](https://www.paddleocr.ai/latest/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL.html).
+        - **PaddleOCR-VL** is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. **This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption**. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. The model has been released on [HuggingFace](https://huggingface.co/PaddlePaddle/PaddleOCR-VL). Everyone is welcome to download and use it! More introduction information can be found in [PaddleOCR-VL](https://www.paddleocr.ai/latest/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL.html).
 
     - **Core Features**:
         - **Compact yet Powerful VLM Architecture**: We present a novel vision-language model that is specifically designed for resource-efficient inference, achieving outstanding performance in element recognition. By integrating a NaViT-style dynamic high-resolution visual encoder with the lightweight ERNIE-4.5-0.3B language model, we significantly enhance the model’s recognition capabilities and decoding efficiency. This integration maintains high accuracy while reducing computational demands, making it well-suited for efficient and practical document processing applications.

tools/infer_rec.py

@@ -120,7 +120,7 @@ def main():
             elif config["Architecture"]["algorithm"] == "SAR":
                 op[op_name]["keep_keys"] = ["image", "valid_ratio"]
             elif config["Architecture"]["algorithm"] == "RobustScanner":
-                op[op_name]["keep_keys"] = ["image", "valid_ratio", "word_positons"]
+                op[op_name]["keep_keys"] = ["image", "valid_ratio", "word_positions"]
             else:
                 op[op_name]["keep_keys"] = ["image"]
         transforms.append(op)
@@ -171,10 +171,10 @@ def main():
                 img_metas = [paddle.to_tensor(valid_ratio)]
             if config["Architecture"]["algorithm"] == "RobustScanner":
                 valid_ratio = np.expand_dims(batch[1], axis=0)
-                word_positons = np.expand_dims(batch[2], axis=0)
+                word_positions = np.expand_dims(batch[2], axis=0)
                 img_metas = [
                     paddle.to_tensor(valid_ratio),
-                    paddle.to_tensor(word_positons),
+                    paddle.to_tensor(word_positions),
                 ]
             if config["Architecture"]["algorithm"] == "CAN":
                 image_mask = paddle.ones(